Biying Fu | Machine Learning Applications | Best Researcher Award

Dr. Biying Fu | Machine Learning Applications | Best Researcher Award

Hochschule RheinMain, Germany

Professional Profile👨‍🎓

Early Academic Pursuits 📚

Dr. Biying Fu’s academic journey began with her primary and secondary education in Shanghai, China, where she developed a strong foundation in subjects like mathematics, German, English, physics, and chemistry. Her academic excellence continued through high school, culminating in an outstanding Abitur score of 1.1. She pursued a Bachelor’s degree in Electrical Engineering and Information Technology at Karlsruhe Institute of Technology (KIT), Germany, followed by a Master’s degree with a focus on Communications and Information Technology. Dr. Fu’s academic pursuits further advanced with a Ph.D. from the Technical University of Darmstadt (TUda), where she specialized in sensor applications for human activity recognition in smart environments.

Professional Endeavors 💼

Dr. Fu’s professional career began at the Fraunhofer Institute for Integrated Circuits (IGD), where she contributed significantly to the Sensing and Perception group. Over time, she expanded her expertise into the Smart Living and Biometric Technologies department, becoming a scientific employee and project leader. Her roles included innovative work on smart environments, human activity recognition, and biometric technologies. Dr. Fu has been instrumental in creating real-world applications for sensor technologies and machine learning algorithms, with notable contributions to the Smart Floor project, which focuses on indoor localization and emergency detection in ambient assisted living environments.

Contributions and Research Focus 🔬

Dr. Fu’s research has been centered around smart environments, sensor systems, and human activity recognition. Her dissertation, titled “Sensor Applications for Human Activity Recognition in Smart Environments,” examined multimodal sensor systems, including capacitive sensors, accelerometers, and smartphone microphones, for tracking physical activities and indoor localization. Her work merges signal processing, neural networks, and data generation techniques to enhance the interaction between people and machines. Her ongoing research continues to explore the intersection of artificial intelligence and explainability, contributing to the development of transparent and interpretable machine learning models.

Impact and Influence 🌍

Dr. Fu’s research in smart technologies has far-reaching implications for assistive technologies, healthcare, and the development of intuitive human-machine interactions. Her contributions to the field of human activity recognition have improved how we use sensors to monitor and analyze daily activities in smart homes and health environments. Furthermore, her involvement in interdisciplinary projects, such as optical quality control and optical medication detection, has enhanced industrial applications of computer vision and machine learning.

Academic Cites and Recognition 📖

Dr. Fu has earned recognition in both academia and industry. Her dissertation, publications, and research projects have attracted attention in top conferences and journals. Her participation in global programs like the Summer School of Deep Learning and Reinforcement Learning at the University of Alberta further emphasizes her standing in the scientific community. Additionally, Dr. Fu has been an active participant in various educational and research initiatives, such as the REQUAS project, and she continues to serve as a professor at Hochschule RheinMain, where she focuses on the emerging field of Explainable AI.

Technical Skills ⚙️

Dr. Fu possesses advanced technical skills in various programming languages, including C/C++, Java, and Python. She is highly proficient in machine learning libraries such as Scikit-Learn, TensorFlow, Keras, and PyTorch, and has hands-on experience with simulation tools like Matlab/Simulink and CST Microwave Studio. Her expertise extends to signal processing, computer vision, and the use of embedded systems for smart applications. Additionally, Dr. Fu has a strong command of version control tools like GIT and SVN, as well as project management platforms such as JIRA and SCRUM.

Teaching Experience 👩‍🏫

Dr. Fu has contributed to the academic development of students through various teaching roles. She has served as a tutor for subjects like digital technology and wave theory during her time at KIT. As a professor at Hochschule RheinMain, she currently teaches courses in smart environments, with a focus on advanced topics in Explainable AI. Her ability to communicate complex concepts clearly and her passion for fostering intellectual curiosity have made her a respected educator in her field.

Legacy and Future Contributions 🌟

Dr. Fu’s work continues to shape the future of smart environments, human-machine interactions, and explainable AI. Her research on sensor technologies and machine learning has paved the way for smarter and more intuitive technologies in healthcare, industry, and everyday life. As she continues to contribute to both academic and industry advancements, Dr. Fu’s legacy will be marked by her dedication to the development of technologies that improve human well-being and promote transparency in artificial intelligence systems. Her ongoing contributions will continue to inspire the next generation of researchers and engineers.

📖Top Noted Publications

A Survey on Drowsiness Detection – Modern Applications and Methods

Authors: Biying Fu, Fadi Boutros, Chin-Teng Lin, Naser Damer

Journal: IEEE Transactions on Intelligent Vehicles

Year: 2024

Generative AI in the context of assistive technologies: Trends, limitations and future directions

Authors: Biying Fu, Abdenour Hadid, Naser Damer

Journal: Image and Vision Computing

Year: 2024

Biometric Recognition in 3D Medical Images: A Survey

Authors: Biying Fu, Naser Damer

Journal: IEEE Access

Year: 2023

Face morphing attacks and face image quality: The effect of morphing and the unsupervised attack detection by quality

Authors: Biying Fu, Naser Damer

Journal: IET Biometrics

Year: 2022

Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-Adaption and Few-Shot Learning

Authors: Biying Fu, Naser Damer, Florian Kirchbuchner, Arjan Kuijper

Journal: Book chapter

Year: 2021

Jing An | Artificial Intelligence | Best Researcher Award

Dr. Jing An | Artificial Intelligence | Best Researcher Award

Yancheng Institute of Technology, China

👨‍🎓Professional Profile

Scopus Profile

👨‍🏫 Summary

Dr. Jing An is a distinguished professor and master tutor at Yancheng Institute of Technology, China. He specializes in intelligent manufacturing engineering, industrial big data fault diagnosis, and residual life prediction. With numerous patents and software copyrights, Dr. An has published over 20 SCI/EI indexed papers and contributed to key academic texts. His pioneering research in artificial intelligence-based fault diagnosis has earned him prestigious awards, including first-place recognition in the China Commerce Federation Science and Technology Award .

🎓 Education

Dr. An holds a Ph.D. in Computer Science from Hohai University (2021) and a Master’s degree in Computer Science from Harbin University of Science and Technology (2006). His academic foundation has strongly influenced his research in AI and fault diagnosis.

💼 Professional Experience

Having joined Yancheng Institute of Technology in 2006, Dr. An is also the Vice President of Science and Technology for Jiangsu Province’s “Double Innovation Plan.” He has led numerous provincial-level projects, and his expertise has extended to more than 10 industry partnerships 🚀.

📚 Academic Citations

Dr. An has authored over 20 peer-reviewed papers in prestigious journals such as IEEE Access and Mathematical Problems in Engineering. His impactful research on AI-based fault diagnosis methods is frequently cited within the academic community .

🔧 Technical Skills

Dr. An is skilled in AI-based fault diagnosis, deep learning, machine learning, and industrial big data analytics. He has expertise in Convolutional Neural Networks (CNNs) and intelligent manufacturing systems, focusing on improving machinery reliability and efficiency .

🧑‍🏫 Teaching Experience

Dr. An has been recognized as an outstanding teacher twice at Yancheng Institute of Technology. He teaches courses in intelligent manufacturing engineering and industrial big data fault diagnosis, preparing students to advance in the field of AI and data science .

🔍 Research Interests

Dr. An’s research is focused on developing intelligent systems for fault diagnosis in rotating machinery, predictive maintenance, and the application of deep learning techniques in industrial big data. His work aims to enhance manufacturing processes and equipment reliability .

📖Top Noted Publications

Hybrid Mechanism and Data-Driven Approach for Predicting Fatigue Life of MEMS Devices by Physics-Informed Neural Networks

Authors: Cheng, J., Lu, J., Liu, B., An, J., Shen, A.

Journal: Fatigue and Fracture of Engineering Materials and Structures

Year: 2024

Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks

Authors: An, J., An, P.

Journal: International Journal of Circuits, Systems and Signal Processing

Year: 2022

Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding

Authors: An, J., Ai, P., Liu, C., Xu, S., Liu, D.

Journal: IEEE Access

Year: 2021

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Riemann Metric Correlation Alignment

Authors: An, J., Ai, P.

Journal: Mathematical Problems in Engineering

Year: 2020

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Authors: An, J., Ai, P., Liu, D.

Journal: Shock and Vibration

Year: 2020

Haixia Mao | Data Science | Best Researcher Award

Dr. Haixia Mao | Data Science | Best Researcher Award

Shenzhen Polytechnic University, China

👨‍🎓Professional Profile

Scopus Profile

👩‍🏫 Summary

Dr. Haixia Mao is an Associate Professor at the School of Automobile and Transportation at Shenzhen Polytechnic University, China. She holds a Ph.D. in Geographic Information System (GIS) and Remote Sensing from the Hong Kong Polytechnic University. Her research focuses on GIS-T, big data analytics, and analyzing urban mobility patterns, using spatial-temporal data from transportation systems to improve urban planning and human behavior modeling.

🎓 Education

Dr. Mao earned her Ph.D. in GIS and Remote Sensing from the Hong Kong Polytechnic University in 2010. She also completed her M.S. in GIS at Wuhan University in 2004 and her B.S. in Photogrammetry and Remote Sensing from Wuhan University.

💼 Professional Experience

Dr. Mao currently serves as an Associate Professor at Shenzhen Polytechnic University (since 2012), where she has contributed to the development of transportation-related GIS courses. Previously, she worked as a Postdoctoral Researcher at the Hong Kong Polytechnic University from 2014 to 2016, furthering her expertise in urban computing and GIS.

📚 Research Interests

Her research encompasses GIS-T (Geographic Information Systems for Transportation), big data analytics, remote sensing, deep learning, and urban planning. She investigates the mobility patterns of citizens through traffic data (including taxis, buses, and metro) and leverages spatial-temporal data to enhance urban infrastructure and predict human behavior.

🏅 Academic Contributions

Dr. Mao has authored over 20 peer-reviewed journal articles and conference papers. She has also been awarded a patent for a caching system with a new cache placement method. She has received research grants for spatial-temporal data analytics in transportation and has actively contributed to major projects in urban computing.

💻 Technical Skills

Dr. Mao’s technical expertise spans GIS, remote sensing, big data analytics, deep learning, and spatial-temporal data analysis. She integrates these advanced techniques to solve complex transportation and urban planning challenges.

📚 Teaching Experience

Dr. Mao has taught several courses since 2012 at Shenzhen Polytechnic University, including GIS for Transportation, Traffic Engineering Cartography, and Traffic Information Processing. She has a long-standing dedication to educating future professionals in the field of urban planning and transportation technology.

 

📖Top Noted Publications

Multiple sclerosis lesions segmentation based on 3D voxel enhancement and 3D alpha matting

Authors: Sun, Y., Zhang, Y., Wang, X., Zeng, Z., Mao, H.
Journal: Chinese Journal of Medical Physics
Year: 2022

Customer attractiveness evaluation and classification of urban commercial centers by crowd intelligence

Authors: Mao, H., Fan, X., Guan, J., Wang, Y., Xu, C.
Journal: Computers in Human Behavior
Year: 2019

 When Taxi Meets Bus: Night bus stop planning over large-scale traffic data

Authors: Xiao, L., Fan, X., Mao, H., Lu, P., Luo, S.
Conference: Proceedings – 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
Year: 2017

Characterizing On-Bus WiFi passenger behaviors by approximate search and cluster analysis

Authors: Jiang, M., Fan, X., Zhang, F., Mao, H., Liu, R.
Conference: Proceedings – 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
Year: 2017

Efficient visibility analysis for massive observers

Authors: Wang, W., Tang, B., Fan, X., Yang, H., Zhu, M.
Conference: Procedia Computer Science
Year: 2017

Web access patterns enhancing data access performance of cooperative caching in IMANETs

Authors: Fan, X., Cao, J., Mao, H., Zhao, Y., Xu, C.
Conference: Proceedings – IEEE International Conference on Mobile Data Management
Year: 2016

Hend ALnajjar | Artificial Intelligence | Best Researcher Award

Assoc Prof Dr. Hend ALnajjar, Artificial Intelligence, Best Researcher Award

Assoc Prof Dr. Hend ALnajjar at king Saud bin Abdulaziz for health science, Saudi Arabia

Professional Profile

Scopus Profile
Orcid  Profile
Research Gate Profile
Google Scholar Profile

🌟Summary

Dr. Hend Abdu Alnajjar is an accomplished Associate Professor in Pediatric Nursing at the College of Nursing–Jeddah, King Saud bin Abdulaziz University for Health Sciences. With a robust educational background including a Ph.D. in Nursing from Manchester University and extensive experience in nursing education, she specializes in maternal and child health, healthcare quality, and academic leadership.

🎓Education

  • Doctor of Philosophy in Nursing, Manchester University, UK (November 2012)
  • MSc in Advanced Nursing Studies, Manchester University, UK (November 2008)
  • Master of Medical Education, College of Medicine, King Saud bin Abdulaziz University for Health Sciences (2017-2019)
  • Bachelor of Science in Nursing, King Abdulaziz University, Jeddah, KSA (September 2000)

💼 Professional Experience

Dr. Hend Abdu Alnajjar brings a wealth of expertise as an Associate Professor in Pediatric Nursing at the College of Nursing–Jeddah, King Saud bin Abdulaziz University for Health Sciences. With a distinguished career spanning leadership roles such as Dean of the College of Nursing – Jeddah and Associate Dean of Academic & Student Affairs, she has significantly contributed to advancing nursing education and healthcare quality in Saudi Arabia. Dr. Alnajjar’s leadership extends to extensive involvement in accreditation processes, curriculum development, and quality assurance initiatives, enhancing educational standards across multiple institutions. Her commitment to maternal and child health is underscored by her clinical background in neonatal intensive care units, further enriching her academic and practical contributions to the nursing profession.

🔬 Research Interests

Dr. Hend Abdu Alnajjar’s research interests are focused on pivotal areas within nursing and healthcare. Her primary scholarly pursuits revolve around maternal and child health, where she explores innovative approaches to improve healthcare outcomes for mothers and children. Additionally, her research encompasses nursing education, aiming to enhance teaching methodologies and curriculum frameworks to foster competent nursing professionals. Dr. Alnajjar is also passionate about healthcare quality, investigating strategies to optimize patient care delivery and safety within clinical settings. Her academic leadership in these domains reflects a commitment to advancing nursing practice and education both locally and globally.

📖 Publication Top Noted

Article title: Digital proficiency: assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students

    • Authors: Ebtsam Aly Abou Hashish, Hend Alnajjar
    • Journal: BMC Medical Education
    • Volume: 24
    • Pages: 508
    • Year: 2024

Article title: Perception of the accreditation of the National Commission for Academic Accreditation and assessment at different health colleges in Jeddah, Saudi Arabia

    • Authors:Ali S Al-Shareef, Mansour A AlQurashi, Azza Al Jabarti, Hend Alnajjar, Ahmad A Alanazi, Mohamed Almoamary, Bader Shirah, Khalid Alqarni
    • Journal: Cureus
    • Volume: 15
    • Year: 2023

Article title: Managerial power bases and its relationship to influence tactics and conflict management styles: Bedside nurses’ perspective

    • Authors: Ebtsam Abou Hashish, Hend Alnajjar, Arwa Al Saddon
    • Journal: Worldviews on Evidence‐Based Nursing
    • Volume: 20
    • Issue: 5
    • Year: 2023

Article title: Exploring the relationship between leadership and conflict management styles among nursing students

    • Authors: Hend Alnajjar, Ebtsam Abou Hashish
    • Journal: Nursing management
    • Volume: 29
    • Issue: 3
    • Year: 2022

Article title: Academic ethical awareness and moral sensitivity of undergraduate nursing students: assessment and influencing factors

    • Authors: Hend Abdu Alnajjar, PhD, Ebtsam Aly Abou Hashish, PhD
    • Journal: SAGE open nursing
    • Volume: 7
    • Year: 2021