Xibing Xu | Data Science | Best Researcher Award

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Dr. Xibing Xu | Data Science | Best Researcher Award

Shenzhen Medical Academy of Research and Translation, China

Author Profiles

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Scopus

Early Academic Pursuits 🎓

Dr. Xibing Xu’s academic journey began with a solid foundation in biological sciences, leading to his Master’s degree from Shandong University in 2012. His Master’s thesis focused on the two-component signal transduction systems of Streptomyces hygroscopicus. This foundational research sparked his interest in bacterial biology and set the stage for his doctoral studies at Sichuan University. Dr. Xu’s Ph.D. thesis, completed in 2015, centered on “Protein homeostasis regulation and ubiquitin-like systems in bacteria,” deepening his expertise in bacterial systems and protein regulation.

Professional Endeavors 💼

his Ph.D., Dr. Xu expanded his research horizons by embarking on various international and prestigious roles. In 2017-2018, he was awarded a visiting scholarship by the China Scholarship Council, enabling him to work as a visiting scholar at the Laboratoire de Microbiologie et Génétique Moléculaire (LMGM) in Toulouse, France. This experience enhanced his research capabilities and fostered a deeper understanding of bacterial molecular mechanisms. From 2020 to the present, Dr. Xu has served as a Postdoctoral Fellow at LMGM, where he leads the ANR project M-Tox, which investigates translational control by HigB1 and MenT toxins in Mycobacterium tuberculosis.

Contributions and Research Focus 🔬

Dr. Xu’s research is at the cutting edge of microbiology, focusing on bacterial growth inhibition mechanisms mediated by toxins in toxin-antitoxin systems. Specifically, his work on deleterious tRNA nucleotidyltransferase toxins, like MenT in Mycobacterium tuberculosis, has unveiled novel bacterial mechanisms that regulate translation and protein homeostasis. His research involves exploring the role of tRNA biology in bacterial growth control and the interaction between tRNA and toxin proteins, a focus that has the potential to inform future therapeutic strategies for bacterial infections. His expertise extends to RNA-seq, enzymology, and protein-RNA/DNA interactions, all of which contribute to a deeper understanding of bacterial survival and virulence.

Impact and Influence 🌍

Dr. Xu’s work has far-reaching implications in microbial Microbiologie and therapeutic development. By identifying key mechanisms that control bacterial growth through toxin-antitoxin systems, his research addresses critical challenges in combating drug-resistant bacterial infections. His investigations into tRNA biology have provided insights into how bacteria regulate protein synthesis, which could lead to the development of novel antimicrobial strategies. His innovative research has garnered attention within the scientific community, reflected in invitations to present at major international conferences such as the 29th International tRNA Conference in 2024.

Academic Citations 📚

Dr. Xu’s contributions to the field have not only enhanced scientific knowledge but have also been widely recognized and cited in academic publications. His work on toxin-antitoxin systems and tRNA nucleotidyltransferase toxins has attracted significant attention in the microbiology and biochemistry fields. As his research continues to influence studies on bacterial translation control, his citation index is expected to grow, cementing his reputation as an expert in bacterial growth regulation and antibiotic resistance mechanisms.

Technical Skills 🛠️

Dr. Xu possesses a broad array of technical skills in biochemistry and molecular biology, enabling him to conduct complex experimental research. He is proficient in RNA-seq library design and construction, protein-DNA/RNA interaction analysis, and toxin protein purification and activity assays. Additionally, he has extensive experience in cell-free transcription and translation assays, essential for studying the mechanistic details of translation regulation in bacteria. His technical prowess underpins the success of his innovative research in bacterial biology and protein homeostasis.

Teaching Experience 👨‍🏫

Dr. Xu has a strong track record in teaching and academic supervision. From 2015 to 2020, he taught various courses such as Medical Biology, Medical Genetics, and Medical Cell Biology at Henan University of Science and Technology. His teaching not only involved extensive classroom hours but also practical experimental courses, allowing students to gain hands-on experience in molecular biology techniques. Since 2024, Dr. Xu has been supervising Master’s students at the University Paul Sabatier in Toulouse, mentoring students on projects related to phage peptides and protein homeostasis in bacteria, ensuring the next generation of scientists is equipped with the necessary research skills.

Legacy and Future Contributions 🌟

Dr. Xu’s current and future research promises to continue his legacy of groundbreaking work in microbiology, particularly in the areas of bacterial toxin-antitoxin systems and translation control. As a postdoctoral fellow and researcher at LMGM, he is leading projects that could revolutionize our understanding of bacterial growth regulation, with direct applications in combating drug-resistant Mycobacterium tuberculosis. His ongoing research, mentorship, and commitment to expanding scientific knowledge will ensure that his contributions to the field of microbiology will continue to grow in impact and influence.

 Top Noted Publications 📖

Title: Inducible auto-phosphorylation regulates a widespread family of nucleotidyltransferase toxins

Authors: Tom J. Arrowsmith, Xibing Xu, Shangze, Ben Usher, Peter Stokes, Megan Guest, Agnieszka K. Bronowska, Pierre Genevaux, Tim R. Blower
Journal: Nature Communications
Year: 2024

Title: MenT nucleotidyltransferase toxins extend tRNA acceptor stems and can be inhibited by asymmetrical antitoxin binding

Authors: Xibing Xu, Ben Usher, C. Gutierrez, R. Barriot, T.J. Arrowsmith, X. Han, P. Redder, O. Neyrolles, T.R. Blower, P. Genevaux
Journal: Nature Communications
Year: 2023

Title: Substrate recognition and cryo-EM structure of the ribosome-bound TAC toxin of Mycobacterium tuberculosis

Authors: Moïse, Emmanuel Giudice, Xibing Xu, Hatice Akarsu, Patricia Bordes, Valérie Guillet, Donna-Joe Bigot, Nawel Slama, Gaetano D’Urso, Sophie Chat et al.
Journal: Nature Communications
Year: 2022

Title: ClpXP-mediated Degradation of the TAC Antitoxin is Neutralized by the SecB-like Chaperone in Mycobacterium tuberculosis

Authors: Pauline Texier, Patricia Bordes, Jyotsna Nagpal, Ambre Julie Sala, Moise Mansour, Anne-Marie Cirinesi, Xibing Xu, David Andrew Dougan, Pierre Genevaux
Journal: Journal of Molecular Biology
Year: 2021

Title: The ubiquitin-like modification by ThiS and ThiF in Escherichia coli

Authors: Xibing Xu, Tao Wang, Yulong Niu, Ke Liang, Yi Yang
Journal: International Journal of Biological Macromolecules
Year: 2019

Title: BAH1 an E3 Ligase from Arabidopsis thaliana Stabilizes Heat Shock Factor σ32 of Escherichia coli by Interacting with DnaK/DnaJ Chaperone Team

Authors: Xibing Xu, Ke Liang, Yulong Niu, Yan Shen, Xuedong Wan, Haiyan Li, Yi Yang
Journal: Current Microbiology
Year: 2018

Wenhai Shi | Predictive Modeling Innovations | Best Researcher Award

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Prof. Wenhai Shi | Energy and Utilities Analytics | Best Researcher Award

Professor at Chang’an University, China

👨‍🎓Author Profiles

🎓 Early Academic Pursuits

Prof. Wenhai Shi’s academic journey is marked by a steadfast dedication to environmental sciences. He earned a Ph.D. in Soil Science (2015-2018) from the University of Chinese Academy of Sciences, Yangling, China, focusing on critical ecological processes. Prior to this, he completed his M.S. in Water Conservancy Engineering (2011-2014) at Guangxi University, Nanning, and a B.S. in Water Resources and Hydropower Engineering (2004-2008) from Xi’an University of Technology. His rigorous academic foundation laid the groundwork for his impactful career in hydrology and soil science.

💼 Professional Endeavors

Prof. Shi has built a robust professional portfolio through progressive academic and industry roles. Currently an Associate Professor at the School of Water and Environment, Chang’an University, Xi’an, he has held positions as a Lecturer and Assistant Professor in esteemed institutions, alongside early-career roles as a Laboratory Technician and Reservoir Dispatcher. These experiences have sharpened his expertise in managing large-scale water resource systems and developing ecohydrological solutions.

🌍 Contributions and Research Focus

Prof. Shi’s research addresses critical environmental challenges, including multi-scale ecohydrological processes, land surface soil and water dynamics, and the nutrient cycle in Earth’s critical zones. He is particularly renowned for investigating soil erosion mechanisms and advancing water conservation techniques. His projects, supported by prestigious bodies like the National Natural Science Foundation of China, focus on organic and inorganic carbon dynamics, hydrological simulation, and water cycle evolution in fragile ecosystems like the Loess Plateau.

🌟 Impact and Influence

Through his contributions, Prof. Shi has significantly influenced both academic and practical domains. As a reviewer for prominent international journals such as Land, Hydrology, and Remote Sensing, he ensures the dissemination of high-quality scientific knowledge. His research has enhanced understanding of hydrological systems and informed policies on sustainable water and soil management.

📚 Academic Cites

Prof. Shi is an active member of leading academic societies, including the China Soil Society and the China Society of Soil and Water Conservation, where he contributes to advancing scientific discourse. As a peer review expert for the National Natural Science Foundation of China and the Ministry of Education, his expertise shapes the future of soil and hydrology research in China and beyond.

💻 Technical Skills

With a diverse skill set, Prof. Shi excels in hydrological modeling, ecohydrological simulations, and spatial variability analysis. His technical acumen is complemented by his proficiency in research tools, project management, and data-driven decision-making in environmental sciences.

🏫 Teaching Experience

An accomplished educator, Prof. Shi has developed and taught both undergraduate and postgraduate courses, such as Soil Mechanics, Modern Hydrology, and Soil and Water Conservation. His innovative teaching methods inspire students to engage deeply with critical environmental issues. Notably, he led his students to secure the National First Prize in the 7th National College Students Water Conservancy Innovation Design Competition.

🌟 Legacy and Future Contributions

Prof. Shi’s accolades, including being recognized as a Young Academic Backbone under the Chang’an Scholars Talent Support Program, underscore his lasting contributions to academia and society. He continues to lead impactful research, mentor the next generation of environmental scientists, and innovate in teaching. Prof. Shi’s vision for the future includes enhancing sustainable practices and advancing ecohydrological resilience globally.

📖Top Noted Publications

An improved method that incorporates the estimated runoff for peak discharge prediction on the Chinese Loess Plateau
    • Authors: Shi, W.; Wang, M.; Li, D.; Li, X.; Sun, M.
    • Journal: International Soil and Water Conservation Research
    • Year: 2023
Effects of Crop Rotation and Topography on Soil Erosion and Nutrient Loss under Natural Rainfall Conditions on the Chinese Loess Plateau
    • Authors: Li, C.; Shi, W.; Huang, M.
    • Journal: Land
    • Year: 2023
An improved MUSLE model incorporating the estimated runoff and peak discharge predicted sediment yield at the watershed scale on the Chinese Loess Plateau
    • Authors: Shi, W.; Chen, T.; Yang, J.; Lou, Q.; Liu, M.
    • Journal: Journal of Hydrology
    • Year: 2022
Revised runoff curve number for runoff prediction in the Loess Plateau of China
    • Authors: Shi, W.; Wang, N.; Wang, M.; Li, D.
    • Journal: Hydrological Processes
    • Year: 2021
Predictions of soil and nutrient losses using a modified SWAT model in a large hilly-gully watershed of the Chinese Loess Plateau
    • Authors: Shi, W.; Huang, M.
    • Journal: International Soil and Water Conservation Research
    • Year: 2021

Biying Fu | Machine Learning Applications | Best Researcher Award

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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

Yanjie Zhu | Algorithm Development | Best Researcher Award

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Prof. Yanjie Zhu | Algorithm Development | Best Researcher Award

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China

👨‍🎓Professional Profiles

🔬 Summary

Prof. Yanjie Zhu is a renowned expert in fast magnetic resonance imaging (MRI) and its applications in medical diagnostics. He specializes in developing innovative imaging techniques through machine learning, deep learning, and model-driven methods. As a Professor at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, he leads cutting-edge research projects focused on cardiovascular imaging, brain health, and intelligent diagnostic tools.

🎓 Education

Prof. Zhu earned his Ph.D. in Circuits and Systems from the Shanghai Institute of Technical Physics, Chinese Academy of Sciences, China (2006-2011). He also holds a B.S. in Electronic Engineering and Information Science from the University of Science and Technology of China, Hefei (2002-2006).

🏫 Professional Experience

Prof. Zhu has a distinguished academic career at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, where he progressed from Assistant Professor (2011-2015) to Associate Professor (2015-2020), and is currently a Professor (2020-present). Additionally, he served as a Visiting Scholar (2017-2018) at Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, under the guidance of Reza Nezafat.

📚 Academic Contributions

Prof. Zhu has published several impactful papers in the field of MRI and medical imaging. Notable works include:

  • “Online reconstruction of fast dynamic MR imaging using deep low-rank plus sparse network,” IEEE CBMS, 2022.
  • “k-space based reconstruction method for wave encoded bSSFP sequence,” CECIT, 2021.
  • “Quantification of pectinate muscles inside left atrial appendage from CT images using fractal analysis,” ICMIPE, 2021.
  • Myocardial Edema Imaging – A Comparison of Three Techniques, ISMRM 2018 (Power Pitch, Magna Cum Laude Merit Award).

🔍 Research Interests

Prof. Zhu’s research interests revolve around model-driven fast MRI techniques, generative model-based adaptive MRI methods, and diffusion tensor and edema imaging for myocardial and brain tissue. He is also focused on intelligent diagnosis for the early detection of stroke-related plaques and other cardiovascular conditions.

💻 Technical Skills

Prof. Zhu is highly skilled in MRI imaging techniques, including fast MRI, cardiac MRI, and brain MRI. He also excels in deep learning and AI-based image reconstruction methods, along with proficiency in medical imaging software and model-driven approaches. His expertise in data analysis and computational methods has contributed to advancing medical applications.

👨‍🏫 Teaching Experience

Throughout his career, Prof. Zhu has mentored and taught graduate students and professionals in the fields of MRI technology, medical imaging, and computational healthcare methods. His research-driven approach has helped develop comprehensive educational materials that support the training of professionals in advanced imaging systems.

📖Top Noted Publications

RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping

Authors: Chiyi Huang, Longwei Sun, Dong Liang, Haifeng Liang, Hongwu Zeng, Yanjie Zhu
Journal: Computers in Biology and Medicine
Year: 2024

High-Frequency Space Diffusion Model for Accelerated MRI

Authors: Chentao Cao, Zhuo-Xu Cui, Yue Wang, Shaonan Liu, Taijin Chen, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: IEEE Transactions on Medical Imaging
Year: 2024

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI

Authors: Zhuo-Xu Cui, Chentao Cao, Yue Wang, Sen Jia, Jing Cheng, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: IEEE Transactions on Medical Imaging
Year: 2024

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

Authors: Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng, Chentao Cao, Xi Xu, Ziwei Liu, Haifeng Wang, Yulong Qi, Dong Liang, Yanjie Zhu
Journal: IEEE Journal of Biomedical and Health Informatics
Year: 2024

High efficiency free-breathing 3D thoracic aorta vessel wall imaging using self-gating image reconstruction

Authors: Caiyun Shi, Congcong Liu, Shi Su, Haifeng Wang, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: Magnetic Resonance Imaging
Year: 2024

Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging

Authors: Yanjie Zhu, Jing Cheng, Zhuo-Xu Cui, et.al.
Journal: IEEE Signal Processing Magazine
Year: 2023

Gebeyehu Belay Gebremeskel | Machine Learning | Editorial Board Member

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Assoc Prof Dr. Gebeyehu Belay Gebremeskel | Machine Learning | Editorial Board Member

Assoc Prof Dr. Gebeyehu Belay Gebremeskel  at  Bahir Dar University, Ethiopia

👨‍🎓 Profiles

🌟 Early Academic Pursuits

Dr. Gebeyehu Belay Gebremeskel’s academic journey began at Alemaya University in Dire Dawa, Ethiopia, where he earned his Bachelor of Science in Computer Science in July 1991. His quest for advanced knowledge led him to London South Bank University in England, where he completed a Master of Science in Advanced Information Technology in January 2001. Under the guidance of Professor Ddembe Williams, he delved into System Dynamics Modeling and Decision Science. Dr. Gebremeskel’s pursuit of deeper expertise continued with a Ph.D. from Chongqing University in China, culminating in June 2013. His dissertation, supervised by Professor Zhongshi He, focused on the integration of Data Mining Algorithms and Multi-Agent Systems with Business Intelligence.

💼 Professional Endeavors

Dr. Gebeyehu’s professional career is distinguished by his impactful roles at Bahir Dar University’s Institute of Technology. As an Associate Professor, he has been instrumental in program accreditation, international conference organization, and curriculum development. His expertise has been further honed through a postdoctoral fellowship at Chongqing University’s College of Automation, focusing on machine learning, big data analytics, and intelligent systems. His commitment to excellence is reflected in his contributions to academic and research training, including programs in GIS, web design, and networking.

🔬 Contributions and Research Focus

Dr. Gebeyehu’s research spans several cutting-edge areas within computer science and engineering. His work in Big Data Analytics emphasizes optimization and precision in performance. In Artificial Intelligence and Machine Learning, he explores neural networks, genetic algorithms, and support vector machines, contributing to advancements in intelligent systems and fault diagnosis. His research in Data Mining includes pattern recognition, outlier detection, and intelligent data systems. Additionally, his focus on Intelligent Systems and Agent Technology involves developing algorithms for multi-agent systems and enhancing system intelligence.

🏆 Accolades and Recognition

Dr. Gebeyehu has received significant recognition for his contributions to academia and research. His roles as conference co-chair and technical program chair for international events like ICAST 2018, 2019, and 2020 highlight his leadership and influence in the field. His research publications in renowned journals, such as the International Journal of Data Science and Analytics, further underscore his impact. His work on big data analytics and neural network models has been widely acknowledged and respected within the academic community.

🌍 Impact and Influence

Dr. Gebeyehu’s influence extends across both academic and practical domains. His research has advanced knowledge in intelligent transportation systems and smart environments. His curriculum development and program accreditation efforts at Bahir Dar University have elevated the quality of education and research opportunities. By mentoring postgraduate students and leading international workshops, Dr. Gebeyehu has significantly impacted the field of computer science and engineering, shaping the future of many students and researchers.

🌟 Legacy and Future Contributions

Dr. Gebeyehu Belay Gebremeskel’s legacy is marked by his pioneering research, extensive teaching experience, and leadership in academia. His contributions to machine learning, big data analytics, and intelligent systems have set a high standard in the field. As he continues to explore new research avenues and educational initiatives, Dr. Gebeyehu is poised to make further advancements and impact future generations of researchers and practitioners. His dedication ensures that his influence will be felt long into the future.

📖 Publications

Architecture and optimization of data mining modeling for visualization of knowledge extraction: Patient safety care