Dr. JOONSOO KIM | Digital Transformation | Best Researcher Award

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Dr. JOONSOO KIM | Digital Transformation | Best Researcher Award

Postdoctoral Researcher, Korea Institute of Construction Technology (KICT), South Korea.

Dr. Joon-Soo Kim is an innovative and skilled researcher in Construction Management, with a strong command of advanced data analytics, machine learning, and environmental sustainability. His academic and research work uniquely integrates engineering with cutting-edge technologies such as text mining and big data to enhance construction safety and efficiency. Currently serving as a Postdoctoral Researcher at the Korea Institute of Civil Engineering and Building Technology (KICT), Dr. Kim is highly regarded for developing reliable decision-making models tailored to complex engineering challenges. His expertise spans from eco-friendly engineering solutions to smart waste management systems, making him a vital contributor to sustainable civil engineering research. 🏗️📊🌿

Publication Profile

Scopus

🎓 Education Background

Dr. Kim earned his Ph.D. in Construction Environment and Energy Engineering from Kyungpook National University, where his dissertation explored road construction environmental load and cost estimation using machine learning. He also holds a Master’s in Construction Systems Engineering with a specialization in Geotechnical and Road Engineering, and a Bachelor’s degree in Civil Engineering—all from the same university. His academic path reflects a strong commitment to applying data-driven insights to real-world infrastructure problems. 🎓📚

💼 Professional Experience

Dr. Kim is currently a Postdoctoral Researcher at KICT, contributing to advancements in civil infrastructure and environmental solutions. Prior to this, he worked at the Intelligent Construction Automation Research Center, Kyungpook National University, for over three years. He also shared his expertise as a lecturer at Daegu University, teaching courses such as Basic Statistics and Construction Management. His combined academic and field experience empowers him to lead high-impact research in civil engineering. 🏢👨‍🏫

🏅 Awards and Honors

Dr. Kim holds registered intellectual property rights, including a software-based Construction Waste Information Management (CWIM) system using QR codes (2023), and a patented Eco-Friendly Value Engineering Decision Analysis System (Patent No. 10-1745567, registered in 2017). These innovations underscore his commitment to enhancing efficiency and sustainability in construction engineering through smart technologies. 🏆📜💡

🔍 Research Focus

Dr. Kim’s primary research areas include construction safety, environmental load management, and project efficiency, with a strong focus on big data, machine learning, and geospatial analysis. He specializes in Value Engineering (VE), Life Cycle Assessment (LCA), and advanced image processing techniques like YOLO for object detection. His multidisciplinary approach supports disaster prevention and promotes green building practices. 🔎🌱📈

📘 Conclusion

Combining strong academic foundations with hands-on innovation, Dr. Joon-Soo Kim continues to make significant strides in the civil and construction engineering fields. His work not only enhances safety and environmental responsibility but also sets a benchmark in leveraging AI-driven methodologies for engineering problem-solving. 👏🌐🚧

📚 Top Publications with 

  1. Image Processing and QR Code Application Method for Construction Safety Management
    Kim, J.-S., Yi, C.-Y., & Park, Y.-J., 2021, Applied Sciences
    📑 Cited by: 18 articles (as per Google Scholar)

  2. Impact Evaluation of Water Footprint on Stages of Drainage Works
    Chen Di, Kim, J.-S., Batagalle V., & Kim, B.-S., 2020, Journal of the Korean Society of Civil Engineers
    📑 Cited by: 6 articles

  3. Characteristics Analysis of Seasonal Construction Site Fall Accident using Text Mining
    Kim, J.-S., & Kim, B.-S., 2019, Journal of the Korea Institute of Construction Engineering and Management
    📑 Cited by: 11 articles

  4. Analysis of the Environmental Load Impact Factors for IPC Girder Bridge Using PCA
    Kim, J.-S., Jeon, J.-G., & Kim, B.-S., 2018, Journal of the Korea Institute of Construction Engineering and Management
    📑 Cited by: 9 articles

  5. Analysis of Fire-Accident Factors Using Big-Data in Construction Areas
    Kim, J.-S., & Kim, B.-S., 2017, KSCE Journal of Civil Engineering
    📑 Cited by: 34 articles

  6. Eco-Friendly Design Evaluation Model Using PEI for Construction Facilities
    Kim, J.-S., & Kim, B.-S., 2017, Journal of the Korean Society of Civil Engineers
    📑 Cited by: 10 articles

  7. Definition of Environmental Cost and Eco-VE Model for Construction Facility
    Kim, M.-J., Kim, J.-S., & Kim, B.-S., 2016, Journal of the Korean Society of Civil Engineers
    📑 Cited by: 7 articles

  8. A Proposal of NIA Model for Eco VE Decision of Construction Facilities
    Kim, J.-S., & Kim, B.-S., 2015, Journal of the Korean Society of Civil Engineers
    📑 Cited by: 12 articles

 

Dr. Raihan ur Rasool | Resource management | Best Researcher Award

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Dr. Raihan ur Rasool | Resource management | Best Researcher Award

Senior Architect/ Quantum Ambassador, IBM Australia; Victoria University, Australia.

Raihan ur Rasool is a seasoned technology leader, currently serving as a Senior Solution Architect and Quantum Ambassador at IBM Australia, while also contributing academically as a Ph.D. supervisor and Advisory Board Member at Victoria University, Melbourne. With over 20 years of combined experience in academia and industry, he has established himself as a notable expert in hybrid cloud computing, distributed systems, and quantum technologies. His impactful innovations in cloud scheduling, big data analytics, and energy-efficient VM distribution have been widely acknowledged and cited across research communities. 🧠💻🌐

Publication Profile

ORCID
Scopus
Google Scholar

📘 Education Background

Raihan ur Rasool holds advanced degrees in Computer Science and Engineering, reflecting a strong academic foundation that supports his expertise in distributed computing and secure network systems. His academic training paved the way for his early involvement in both innovative research projects and cutting-edge industrial applications. 🎓📚

💼 Professional Experience

Professionally, Raihan has held several prominent positions in tech innovation. At IBM, he plays a critical role as a Quantum Ambassador, leading research and outreach in quantum computing technologies. His affiliation with Victoria University allows him to mentor Ph.D. students and contribute to strategic academic decisions. His collaborations with renowned scholars like Ian Foster and Andrew Chien from the University of Chicago, and Hua Wang from Victoria University, speak to his influential standing in the global research landscape. 🏢🔬🌍

🏆 Awards and Honors

His scholarly impact is underscored by an h-index of 21, and a publication record of over 80 papers, with around 50 published in reputed journals including IEEE, Elsevier, and Springer. His contributions have earned industry and academic recognition, and he is also a published author (ISBN: 1466697679). 📖🥇

🔬 Research Focus

Raihan’s research spans across distributed systems, network security, big data analytics, IoT, quantum computing, and software-defined networking. His work is known for its practical implications in disaster management, healthcare systems, and cloud infrastructure, often integrating AI and machine learning techniques for optimized system performance. 🔐📊☁️

✅ Conclusion

Raihan ur Rasool is a distinguished researcher and technology innovator whose work bridges the gap between academic theory and industrial application. His leadership in emerging areas like quantum computing and 6G-enabled healthcare analytics positions him as a top contender for recognition in international research awards. 🌟🚀🧬

📚 Top Publications with Notes

  1. Leveraging 6G, extended reality, and IoT big data analytics for healthcare: A review
    Cited by: 15+
    An in-depth review of futuristic healthcare systems combining 6G, XR, and IoT analytics.

  2. Quantum Computing for Healthcare: A Review
    Cited by: 20+
    Explores the potential of quantum technologies in transforming healthcare delivery and diagnostics.

  3. A multi-objective grey-wolf optimization based approach for scheduling on cloud platforms
    Cited by: 5+
    Proposes a novel cloud scheduler improving resource allocation using grey-wolf optimization.

  4. A Hybrid Machine Learning Model for Efficient XML Parsing
    Cited by: 3+
    Introduces a hybrid ML model for faster and more efficient XML parsing in data-heavy applications.

  5. CyberPulse++: A machine learning‐based security framework for detecting link flooding attacks in software defined networks
    Cited by: 30+
    Presents a robust cybersecurity framework for SDNs using ML-driven detection.

  6. Big data analytics enhanced healthcare systems: a review
    Cited by: 100+
    Highly cited work evaluating big data’s role in healthcare innovations.

  7. Complementing IoT Services through Software Defined Networking and Edge Computing: A Comprehensive Survey
    Cited by: 120+
    Recognized for detailing SDN and edge computing synergies for IoT applications.

  8. Feature Selection Optimization in Software Product Lines
    Cited by: 50+
    Improves product line configuration through optimization-based feature selection.

  9. A survey of link flooding attacks in software defined network ecosystems
    Cited by: 80+
    An authoritative survey of LFA threats and countermeasures in SDN environments.

  10. Cyberpulse: A Machine Learning Based Link Flooding Attack Mitigation System for Software Defined Networks
    Cited by: 60+
    ML-based real-time solution for detecting and mitigating LFA in SDN systems.

 

Dr. Yuxiang Leng | Electrical engineering | Best Researcher Award

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Dr. Yuxiang Leng | Electrical engineering | Best Researcher Award

Ph.D, Chongqing University, China

Yuxiang Leng is an emerging researcher in the field of 3D laser point cloud technology and power transmission systems. He is currently pursuing his Ph.D. at the State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, China. With a strong academic background and an innovative mindset, Leng has actively contributed to the advancement of computer vision applications in power systems. He has published eight academic papers and presented his work at high-level international conferences, gaining recognition in both academia and industry for his contributions to smart grid technologies and engineering digitalization.

Publication Profile

Scopus

🎓 Education Background:

Yuxiang Leng earned his B.Sc. degree from Jiangsu University of Science and Technology in 2019 and his M.S. degree from Wenzhou University in 2022. He is currently a Ph.D. candidate at Chongqing University, focusing on advanced research in the digital management of power substations using 3D vision and laser scanning methods. His educational path reflects a consistent pursuit of excellence in engineering and technology.

💼 Professional Experience:

Though still in the early stages of his research career, Yuxiang has shown remarkable productivity. He has been actively involved in eight research projects and has taken a lead role in multiple academic studies. His technical work centers around developing high-precision measurement techniques and improving data accuracy in laser-based 3D reconstruction. While he has not yet been involved in consultancy or editorial positions, his practical approach and novel methodologies have already earned him significant attention in academic forums.

🏅 Awards and Honors:

While specific awards are not listed, Yuxiang’s recognition as a leading contributor to peer-reviewed SCI/EI journals, including being the first author in three high-impact publications, underlines his academic excellence. Additionally, his oral presentations at top-tier international conferences have positioned him as a rising talent in power equipment digitalization. His selection as an IEEE Student Member further reflects his commitment to research and innovation.

🔬 Research Focus:

Yuxiang’s research lies at the intersection of 3D computer vision, laser point cloud data analysis, and power transmission and transformation systems. His most notable contribution involves a dynamic compensation method that significantly reduces vibration-induced errors in 3D point cloud data, enhancing segmentation and reconstruction precision by over 70%. His work not only advances digital twin modeling but also supports smart maintenance, monitoring, and intelligent management of electrical substations.

🔚 Conclusion:

Yuxiang Leng stands out as a dynamic and promising young researcher whose innovative solutions are driving the future of power system digitalization. With a strong publication record, hands-on project experience, and a clear research vision, he is a fitting candidate for the Best Researcher Award. His blend of academic rigor and practical innovation ensures a meaningful impact in the realm of intelligent power grid technology.

📚 Top Publications of Yuxiang Leng

  1. Dynamic compensation approach for mitigating vibration interference in 3D point cloud data of electrical equipment
    Published: 2025
    Journal: Advanced Engineering Informatics
    Cited by: 3 articles

  2. Intelligent Early Warning System for Power Operation Safety Based on Laser Point Cloud Sensing
    Published: 2024
    Conference Paper: International Conference on Smart Energy Systems
    Cited by: 2 articles

  3. LoRaWAN Network Downlink Routing Control Strategy Based on the SDN Framework and Improved ARIMA Model
    Published: 2023
    Journal: Journal of Communications and Networks
    Cited by: 1 article

  4. Contactless Voltage Measurement Considering Spatially Dependent Voltage Compensation
    Published: 2023
    Conference Proceedings: IEEE Power & Energy Society Meeting
    Cited by: 0 articles

 

Assist. Prof. Dr. Chung-Hao Huang | Microwave antenna | Best Researcher Award

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Assist. Prof. Dr. Chung-Hao Huang | Microwave antenna | Best Researcher Award

Assistant Professor, Department of Electrical Engineering/ Chung Yuan Christian University, Taiwan.

Dr. Chung-Hao Huang is an Assistant Professor at the Department of Electrical Engineering, Chung Yuan Christian University, Taiwan, since August 2022. With a passion for cutting-edge technological innovation, Dr. Huang integrates 5G antenna systems, artificial intelligence, and immersive multimedia into his research and teaching. His expertise spans digital learning, mixed reality applications, and smart technologies, contributing actively to both academia and industry through collaborative projects and international events. 🧠📡🎓

Publication Profile

ORCID

🎓 Education Background

Dr. Huang earned his Ph.D. from the Engineering and Technology Research Institute at the National Yunlin University of Science and Technology. His academic journey laid the foundation for his later innovations in antenna design and immersive technology. His strong educational background supports his diverse skill set in emerging technologies and engineering disciplines. 📘🎓

💼 Professional Experience

Before joining academia, Dr. Huang served as an Assistant Engineer at the Institute of Nuclear Energy Research under Taiwan’s Atomic Energy Council from October 2011 to July 2022. There, he contributed significantly to the development of remote-controlled robotic arms and AR-based navigation systems for nuclear decommissioning. Since 2022, at Chung Yuan Christian University, he has led advanced projects on AI-based antenna optimization, heat-dissipative antenna arrays, and metaverse applications in robotics training. 🧑‍🏫🔧🤖

🏅 Awards and Honors

Dr. Huang has been recognized for his leadership in organizing international seminars and industry-university collaborations. Though specific honors were not listed, his work as a principal investigator and frequent conference host reflects his active and respected role in the engineering research community. 🏆🌐

🔍 Research Focus

Dr. Huang’s research focuses on 5G antenna design, RF passive component optimization, digital learning environments, and immersive multimedia such as VR/AR/MR. He also explores the application of artificial intelligence in technical design and educational systems, contributing to smarter, more responsive systems in both academia and industry. His interdisciplinary expertise enables innovation across hardware and software interfaces. 📡🧠🌍

🔚 Conclusion

Dr. Chung-Hao Huang stands at the forefront of technological advancement, blending theoretical rigor with practical implementation. His multifaceted background and commitment to intelligent systems make him a valuable contributor to the future of engineering and digital transformation. 🚀📘💡

📄 Top Publication Note

Title: Design of Laptop Computer Antenna for Wi-Fi 6E Band
Authors: Chung-Hao Huang, Ying-Chao Hong, Sen-Yu Liao, Yi-Chi Chen
Published in: Engineering Proceedings, Year: 2025
DOI: 10.3390/engproc2025092095

Mr. Nikolaos Bouzianis | Medical Physics | Best Researcher Award

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Mr. Nikolaos Bouzianis | Medical Physics | Best Researcher Award

Medical Physicist, P.G.N. Alexandroupolis, Greece.

Nikolaos Bouzianis is a dedicated Medical Physicist and Ph.D. candidate at the Democritus University of Thrace, School of Medicine. With a strong academic foundation in medical physics and hands-on clinical experience in nuclear medicine, Nikolaos has continuously demonstrated a passion for applying advanced technologies—particularly artificial intelligence—to optimize diagnostic imaging and patient care. He holds national certifications as a Medical Physics Expert and Radiation Protection Expert, highlighting his commitment to excellence and safety in medical environments.

Publication Profile

ORCID

🎓 Education Background

Nikolaos is currently pursuing a Ph.D. in Medical Physics (2023–Present) at the Democritus University of Thrace, where his research focuses on leveraging AI algorithms to enhance practices in nuclear medicine. He earned his Master’s degree in Medical Physics (2015–2017) from the University of Patras with an “Excellent” distinction for his thesis on HDL image classification via application development. He also holds a Bachelor’s degree in Physics (2007–2015) from the same university, where he specialized in electronics, computers, and signal processing.

🧪 Professional Experience

Since January 2021, Nikolaos has been working as a Medical Physicist at the University General Hospital of Alexandroupolis, applying his knowledge in clinical settings. He also completed a residency as a Medical Physicist at the University General Hospital of Patras from November 2018 to October 2019. His roles have equipped him with practical exposure to diagnostic imaging, radiation safety, and computational medical physics applications.

🏅 Awards and Honors

Nikolaos holds multiple professional certifications, including a license to practice as a Hospital Physicist in both ionizing and non-ionizing radiation fields. He is recognized as a Certified Medical Physics Expert and Radiation Protection Expert. These honors reflect his high competency level and his respected status in the field of medical physics.

🔬 Research Focus

Nikolaos’s primary research interests lie at the intersection of artificial intelligence and nuclear medicine. His Ph.D. work explores how AI algorithms can optimize existing medical practices, focusing especially on enhancing the quality of scintigraphic imaging while reducing radiation dose. His work exemplifies innovation in digital medical technology, particularly through the use of convolutional autoencoders and advanced image processing techniques.

🔚 Conclusion

With a blend of academic rigor, clinical experience, and a tech-forward mindset, Nikolaos Bouzianis stands out as a promising figure in the field of medical physics. His contributions to AI-based solutions in nuclear medicine aim to redefine diagnostic standards and improve patient outcomes, positioning him as a valuable asset to both research and healthcare communities.

📚 Publication Top Notes

🔹 Title: Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising
🔹 Journal: J. Imaging
🔹 Year: 2025
🔹 Volume/Issue: 11(6), Article 197
🔹 Cited by: 2 articles (as per current indexing)

Mr. Xiang Fang | Ransomware detection | Best Researcher Award

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Mr. Xiang Fang | Ransomware detection | Best Researcher Award

Phd student, The City College of New York, United States.

Xiang Fang is a dedicated and technically proficient researcher in electrical and computer engineering, currently pursuing his Ph.D. at The City College of New York. With a multifaceted background combining hardware, software, cybersecurity, and AI-powered applications, Xiang stands out for his innovative approaches to problem-solving, especially in areas like control systems, image processing, and ransomware detection. His contributions span multiple international presentations, academic projects, and scholarly publications, reflecting a deep passion for research and a commitment to academic excellence. 🌍🔬

Publication Profile

Google Scholar

🎓Education Background

Xiang earned his Ph.D. in Electrical Engineering from The City College of New York (2020–2025), maintaining an impressive GPA of 3.60. Complementing his technical expertise, he acquired an MBA from Academic Europe Open University in April 2025, equipping him with valuable leadership and management skills. His journey began with an M.Sc. in Electrical and Computer Engineering from Purdue University Northwest (2017–2019), followed by a B.Sc. in Electrical and Information Engineering from Shaanxi University of Technology, China (2013–2017). 🎓📘📈

💼Professional Experience

Xiang has actively contributed to academia through teaching assistantships and tutoring roles. At The City College of New York, he worked as a Teaching Assistant for Healthcare Cybersecurity Pathways (2023) and graded Communication Theory (2025). His earlier experience includes tutoring English to middle school students (2013–2014). In research, he has led and contributed to multiple technical projects—ranging from digital image enhancement and SLAM systems to cloud-based secure applications and real-time ransomware detection using anomaly-based methods. 💡🖥️🧪

🏅Awards and Honors

Xiang’s commitment to excellence is reflected in his achievements, including the Certificate of Completion in Children and Climate Change, and the CodePath Cybersecurity Course Certificate. His research has been widely recognized through poster presentations at prestigious forums such as AFRL-CUNY, The Grove School of Engineering Expo, and the Defense & Intelligence Research Forum. 🏆📜🎖️

🔍Research Focus

Xiang’s primary research interests lie in cybersecurity, autonomous systems, signal processing, and AI-powered detection systems. His standout contributions include anomaly-based and honeyfile-based detection mechanisms for crypto ransomware, SLAM systems, and data visualization methods. Leveraging tools like MATLAB, Python, Kalman filters, and machine learning models such as LSTM, Xiang effectively bridges theoretical concepts with practical implementations. 🔐🤖📊

🔚Conclusion

A forward-thinking and results-driven scholar, Xiang Fang exemplifies the blend of innovation, technical acumen, and academic rigor. His trajectory, from hands-on hardware projects to advanced research in digital security, positions him as a rising star in the field of electrical and computer engineering. As he continues his journey, Xiang remains committed to tackling emerging global challenges with smart, scalable, and secure solutions. 🚀📡🌐

📚Top Publications Notes

  1. Crypto-Ransomware Detection Through a Honeyfile-Based Approach with R-Locker
    📅 Published: 2025 | 📘 Journal: MDPI, Mathematics | 📑 Cited by: 5 articles (as of 2025)

  2. Poster: Crypto Ransomware Detection Through a Honeyfile-Based Approach with R-Locker
    📅 Presented: Feb. 2025 | 📍 The Grove School of Engineering Expo, NY, USA

  3. Poster: Anomaly-Based Approach for Crypto Ransomware Detection
    📅 Presented: Nov. 2023 | 📍 AFRL-CUNY Technology and Workforce Development Forum

  4. Poster: Ransomware Detection Methodology
    📅 Presented: May 2022 | 📍 Defense & Intelligence Research Forum, NY, USA

  5. Thesis: Visualization of Mobile Robot Localization and Mapping
    📅 Published: April 2017 | 📍 Purdue University Northwest

  6. Thesis: Design of High-Precision Laser Engraving Platform Control System Based on STC12C5608AD
    📅 Published: June 2017 | 📍 Shaanxi University of Technology

 

Dr. XueHua Zhao | Automation | Best Researcher Award

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Dr. XueHua Zhao | Automation | Best Researcher Award

Student, Northwestern Polytechnical University, China.

Dr. XueHua Zhao 🎓 is a dedicated Chinese researcher specializing in advanced filtering algorithms, sensor networks, and non-Gaussian noise modeling. With a strong mathematical foundation and a focus on artificial intelligence in navigation systems, Dr. Zhao has published several impactful research papers in top-tier international journals and conferences. Currently pursuing a Ph.D. in Computer Science and Technology at Northwestern Polytechnical University, her contributions to maximum correntropy filtering and its distributed applications are widely recognized across the control engineering community 🌐.

Publication Profile

Scopus

📘 Education Background

Dr. Zhao began her academic journey with a Bachelor’s degree in Mathematics Education from Henan Normal University (1998–2002) 🧮. She then pursued a Master’s degree in Computational Mathematics at Guizhou Normal University (2004–2006) 📊. Continuing her academic advancement, she embarked on doctoral research in Computer Science and Technology at Northwestern Polytechnical University from 2016 onwards 🖥️, focusing on advanced estimation and filtering techniques.

💼 Professional Experience

With a foundation in mathematics and applied computing, Dr. Zhao has actively contributed to the scientific community through collaborative projects in signal processing and navigation systems 🚀. She has co-authored papers with experts in both academic and industrial research groups, focusing on algorithms like the Unscented Particle Filter and Sparrow Search Algorithm, highlighting her interdisciplinary approach and engineering insight 🤝.

🏅 Awards and Honors

While specific individual awards have not been explicitly mentioned, Dr. Zhao’s selection as a lead author in several high-impact journals and IEEE conferences reflects peer recognition and commendation from the academic community 🌟. Her work has drawn citations from related research in robust control, navigation systems, and sensor networks 🏆.

🔬 Research Focus

Dr. Zhao’s research interests lie at the intersection of control engineering and computational intelligence 🧠. She focuses on robust estimation methods like the Maximum Correntropy Kalman Filter (MCKF), Rational-Quadratic Kernels, and Particle Filtering under non-Gaussian and censored environments. Her work is crucial in advancing INS/GPS integrated navigation, distributed sensor fusion, and optimization algorithms for real-world uncertainty modeling and adaptive control systems 🔍.

🔚 Conclusion

Dr. XueHua Zhao continues to make meaningful contributions to control theory and intelligent filtering under uncertainty. Her deep mathematical insight, algorithmic innovation, and collaborative research spirit position her as a valuable contributor to global advancements in nonlinear filtering and smart navigation technologies 🌐📈.

📚 Publication Top Notes:

  1. Stochastic Stability of the Improved Maximum Correntropy Kalman Filter against Non-Gaussian Noises, International Journal of Control, Automation and Systems, 2024, 22(3): 731–743.
    Cited by: 6 articles

  2. Rational-Quadratic Kernel-Based Maximum Correntropy Kalman Filter for the Non-Gaussian Noises, Journal of the Franklin Institute, 2024, 361(17): 107286.
    Cited by: 4 articles

  3. Distributed Maximum Correntropy Linear Filter Based on Rational-Quadratic-kernel against Non-Gaussian Noise, Symmetry, 2025 (in press).
    Cited by: Awaiting citation

  4. A Fading Factor Unscented Particle Filter and Its Application in INS/GPS Integrated Navigation, ICISCE 2017 Proceedings, IEEE, 2017: 792–796.
    Cited by: 19 articles

  5. Adaptive Robust Unscented Particle Filter and Its Application in Sins/Sar Integration Navigation System, IAEAC 2017 Proceedings, IEEE, 2017: 2364–2368.
    Cited by: 21 articles

  6. Enhanced Sparrow Search Algorithm Based on Improved Game Predatory Mechanism and Its Application, Digital Signal Processing, 2024, 145: 104310.
    Cited by: 5 articles

  7. Linear and Nonlinear Filters Based on Statistical Similarity Measure for Sensor Network Systems, Journal of the Franklin Institute, 2025, 362(1): 107412.
    Cited by: Awaiting citation

  8. Random weighted adaptive filtering and its application in integrated navigation , Journal of Projectiles, Rockets, Missiles and Guidance , 2017 , 37(05): 1–5+10.
    Cited by: 12 articles

 

Mr. Abdullah Al Mamun | Deep Learning | Young Scientist Award

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Mr. Abdullah Al Mamun | Deep Learning | Young Scientist Award

Lecturer, Dhaka University of Engineering & Technology (DUET), Gazipur, Bangladesh

Abdullah Al Mamun is a passionate researcher and academic professional specializing in Internet of Things (IoT), Machine Learning 🤖, and Explainable Artificial Intelligence (XAI). Currently pursuing his Master of Science in Engineering at Dhaka University of Engineering & Technology (DUET), Gazipur, he brings a vibrant combination of theoretical knowledge and hands-on research experience. His dynamic involvement in projects across sustainability, computer vision 🧠, and intelligent systems has positioned him as a promising contributor to the technology and research domain.

Publication Profile

ORCID

🎓 Education Background

Abdullah Al Mamun is presently pursuing his M.Sc. in Computer Science and Engineering at DUET, Gazipur (since October 2024), where he has already completed his Bachelor of Science in Computer Science and Engineering with distinction in 2024 🎓. His consistent academic journey showcases his dedication to computing, innovation, and advanced research.

💼 Professional Experience

Mamun is currently working as a Lecturer at the Department of CSE, Model Institute of Science and Technology, Gazipur, while also serving as a part-time Research Assistant in the Multimedia Signal & Image Processing research group at Woosong University, South Korea 🌏. With three years of tutoring experience at ACME DUET Admission Coaching Center, and two internships in web development and CMS technologies, he has gained broad teaching, mentoring, and development experience across various platforms 🖥️. His administrative roles in DUET Career & Research Club and DUET Computer Society also underscore his leadership and community contributions.

🏆 Awards and Honors

Abdullah has been recognized for his academic and problem-solving excellence. He earned the “Second Runner-Up” at BEYOND THE METRICS-2023 hosted by IUT (OIC), and “Runner-Up” at the Intra DUET Programming Contest (IDPC) 2022 🏅. He has actively participated in events like NASA Space App Challenge 2024 and DUET TECH FEST-2023, reflecting his engagement in competitive and innovation-driven activities 🚀.

🔬 Research Focus

Abdullah’s core research interests lie in IoT and sustainability, Machine Learning, Computer Vision, Explainable AI, and Reinforcement Learning 🧠📡. He has been instrumental in implementing real-world projects such as IoT-based energy monitoring systems and child safety monitoring, defect detection via XAI, and skin cancer classification using optimized deep learning models. His collaborative projects with global research teams exhibit his strong contribution to the evolving field of intelligent systems and digital transformation.

✅ Conclusion

With an impressive blend of academic rigor, technical skills, and collaborative research experience, Abdullah Al Mamun is making impactful strides in the field of computer science 🧩. His work exemplifies innovation, sustainability, and intelligence in engineering systems. He continues to grow as a researcher dedicated to contributing to global scientific advancements 🌐.

📚 Top Publications 

  1. Developed an IoT-based Smart Solar Energy Monitoring System for Environmental Sustainability – 3rd International Conference on Advancement in Electrical and Electronic Engineering, 2024.
    Cited by: 7 articles 📑

  2. Developing an IoT-based Child Safety and Monitoring System: An Efficient Approach – IEEE 26th International Conference on Computer and Information Technology (ICCIT), 2023.
    Cited by: 13 articles 🔐

  3. Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques – IEEE Journal, 2024.
    Cited by: 15 articles ⚙️

  4. IoT-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8 – MDPI Sensors Journal, 2023.
    Cited by: 10 articles 🚗

  5. Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN – 2nd NCIM Conference, Bangladesh, 2024.
    Cited by: 5 articles 🧬

  6. Enhancing DBSCAN Dynamically: A Novel Approach to Parameter Initialization and Outlier Reduction – Bachelor Thesis, DUET, 2024.
    Cited by: 3 articles 🔍

 

Prof. Dr. Jörg Schäfer | Machine Learning | Best Researcher Award

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Prof. Dr. Jörg Schäfer | Machine Learning | Best Researcher Award

Professor, Frankfurt University of Applied Sciences, Germany

Professor Dr. Jörg Schäfer is a renowned academic and researcher in the field of Computer Science, currently serving at the Frankfurt University of Applied Sciences in Germany. With a distinguished background in mathematics and a dynamic career bridging academia and industry, Dr. Schäfer is celebrated for his expertise in object-oriented programming, distributed systems, databases, and machine learning. His innovative research in artificial intelligence and human activity recognition, paired with decades of experience in technology strategy and complex system architecture, have made him a leading figure in both academic and professional circles.

Publication Profile

🎓 Education Background:

Dr. Schäfer completed his Ph.D. in Mathematics with summa cum laude at Ruhr-Universität Bochum (1991–1993) under the supervision of Prof. Dr. Sergio Albeverio. His doctoral work was part of the elite DFG graduate program “Geometrie und Mathematische Physik” and included an academic travel scholarship to Japan. Before his Ph.D., he earned a diploma in Mathematical Physics with distinction from Ruhr-Universität Bochum (1987–1991), laying the groundwork for his future interdisciplinary research.

💼 Professional Experience:

Dr. Schäfer’s professional career blends deep academic involvement with high-impact industry roles. Since 2009, he has been a professor at Frankfurt University of Applied Sciences, teaching subjects such as object-oriented programming, distributed systems, and machine learning. He is the founding member of the Industrial Data Science (INDAS) research group and serves as Chairman of the B.Sc. Computer Science program. Prior to his academic tenure, Dr. Schäfer held senior positions at Accenture (2005–2009) and Cambridge Technology Partners (2000–2005), where he was responsible for large-scale architecture design, pre-sales, delivery, and enterprise integration strategies. His early career includes project management roles at Westdeutsche Landesbank and a trainee program at Salomon Brothers, as well as scientific assistant roles focused on stochastic analysis.

🏅 Awards and Honors:

Professor Schäfer has received several prestigious accolades throughout his career. Most notably, he was awarded the Hessischer Hochschulpreis in 2022 for excellence in teaching. During his academic formation, he was also a scholar of the Studienstiftung des deutschen Volkes (1987–1991), reflecting his outstanding academic promise from an early stage.

🔬 Research Focus:

Dr. Schäfer’s research is focused on artificial intelligence, machine learning, mobile and distributed systems, and human activity recognition. His work leverages WiFi channel state information (CSI) for device-free activity detection, contributing significantly to the field of pervasive computing. He also has a foundational background in mathematical physics, particularly in Chern–Simons theory and stochastic analysis, which informs his unique approach to computer science problems.

🧩 Conclusion:

With a remarkable blend of academic rigor and real-world application, Professor Dr. Jörg Schäfer stands out as a multifaceted scholar and technology leader. His research continues to shape the future of data science and AI-driven systems, while his dedication to teaching and mentorship inspires the next generation of computer scientists.

📚 Top Publications

  1. Computer-implemented method for ensuring the privacy of a user, computer program product, device
    J Schäfer, D Toma
    US Patent 8,406,988, 2013
    Cited by: 237 articles

  2. Device free human activity and fall recognition using WiFi channel state information (CSI)
    N Damodaran, E Haruni, M Kokhkharova, J Schäfer
    CCF Transactions on Pervasive Computing and Interaction, 2020
    Cited by: 109 articles

  3. Human activity recognition using CSI information with nexmon
    J Schäfer, BR Barrsiwal, M Kokhkharova, H Adil, J Liebehenschel
    Applied Sciences, 2021
    Cited by: 75 articles

  4. Abelian Chern–Simons theory and linking numbers via oscillatory integrals
    S Albeverio, J Schäfer
    Journal of Mathematical Physics, 1995
    Cited by: 53 articles

  5. A rigorous construction of Abelian Chern-Simons path integrals using white noise analysis
    P Leukert, J Schäfer
    Reviews in Mathematical Physics, 1996
    Cited by: 43 articles

  6. Fall detection from electrocardiogram (ECG) signals and classification by deep transfer learning
    FS Butt, L La Blunda, MF Wagner, J Schäfer, I Medina-Bulo, et al.
    Information, 2021
    Cited by: 40 articles

  7. Device free human activity recognition using WiFi channel state information
    N Damodaran, J Schäfer
    2019 IEEE SmartWorld Conference
    Cited by: 37 articles

  8. Cloud computing – Evolution in der Technik, Revolution im Business
    G Münzl, B Przywara, M Reti, J Schäfer, et al.
    Berlin: BITKOM, 2009
    Cited by: 37 articles

 

Mr. Muhammad Tauqeer Iqbal | Machine Learning | Best Researcher Award

Published on by Computer Scientists Awards

Mr. Muhammad Tauqeer Iqbal | Machine Learning | Best Researcher Award

Mr. Muhammad Tauqeer Iqbal , Yangzhou University, China

Iqbal Muhammad Tauqeer is a passionate researcher and master’s student at Yangzhou University, China , specializing in the domain of Machine Learning 🤖. With a solid foundation in both industry and academia, he has combined practical management experience with cutting-edge AI research. His dedication to data science applications and computer vision has led to a notable publication recognized as a best paper, showcasing his potential in the rapidly evolving tech landscape 🌟.

Professional Profile

ORCID

🎓 Education Background

Iqbal is currently pursuing his Master’s degree at Yangzhou University, China 📚, where his academic focus is on machine learning and its applications in computer vision. His academic pursuits have been driven by a commitment to advancing AI-driven solutions in environmental monitoring and digital recognition systems.

💼 Professional Experience

Before his transition into research, Iqbal gained valuable industry experience as an Assistant Production Manager at OPPO Mobile Company Pakistan 📱 for over two years. This role provided him with deep insights into production workflows and industry standards, bridging the gap between theoretical learning and practical application.

🏆 Awards and Honors

Iqbal’s research has already earned accolades, with his paper titled “A Transfer Learning-Based VGG-16 Model for COD Detection in UV–Vis Spectroscopy” being recognized as a Best Paper 🥇. This early recognition is a testament to the impact and novelty of his contributions to AI-powered environmental diagnostics.

🔬 Research Focus

His research interests lie primarily in Machine Learning, Deep Learning, Transfer Learning, and Computer Vision 🧠📊. He is particularly focused on applying these techniques to UV–Vis Spectroscopy and digital display recognition. He is currently working on a second research project that extends his work in pattern recognition and visual AI.

🔚 Conclusion

With a unique blend of industrial management experience and academic rigor, Iqbal Muhammad Tauqeer is emerging as a promising contributor to the field of Artificial Intelligence. His work in machine learning models for environmental monitoring reflects not only his technical skills but also his commitment to impactful innovation 🌍🔍.

📚 Publication Top Note

  1. Title: A Transfer Learning-Based VGG-16 Model for COD Detection in UV–Vis Spectroscopy
    Journal: Journal of Imaging
    Publisher: MDPI
    Published Year: 2025