Minling Zhu | Artificial Intelligence | Best Researcher Award | 13405

Assoc Prof Dr. Minling Zhu | Artificial Intelligence | Best Researcher Award 

Assoc Prof Dr. Minling Zhu, Beijing Information Science and Technology University, China

Assoc. Prof. Dr. Minling Zhu is an esteemed faculty member at the College of Computer Science, Beijing Information Science and Technology University, China. She earned her Ph.D. from Beihang University in 2012 and has since made significant contributions to the fields of Artificial Intelligence and Embedded Systems. A Senior Member of the Chinese Association for Artificial Intelligence and a member of several key national technical and educational committees, Dr. Zhu plays an active role in shaping China’s AI research and smart education development.

Profile

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🌱 Early Academic Pursuits

Assoc. Prof. Dr. Minling Zhu began her academic journey with a passion for computational systems and intelligent technologies. Her formative years were characterized by a deep curiosity in how machines learn and interact with real-world environments. This passion led her to pursue advanced studies in computer science, culminating in a Ph.D. from Beihang University, one of China’s most prestigious institutions in engineering and technology, in 2012. During her doctoral research, she laid the foundation for her future exploration into Artificial Intelligence (AI) and Embedded Systems, gaining both theoretical expertise and practical acumen.

🧑‍🏫 Professional Endeavors

Following the completion of her doctoral degree, Dr. Zhu joined the College of Computer Science at Beijing Information Science and Technology University (BISTU) as an Associate Professor. In this role, she has been actively involved in both undergraduate and postgraduate teaching, contributing significantly to the university’s mission of nurturing the next generation of AI and embedded systems professionals.

Dr. Zhu is also engaged in administrative, curriculum development, and mentoring activities, embodying the values of academic leadership and institutional development. She consistently integrates cutting-edge research into her classroom, ensuring that students benefit from real-time technological progress.

🔬 Contributions and Research Focus

Dr. Minling Zhu’s research work stands at the intersection of Artificial Intelligence and Embedded Systems, two rapidly evolving domains with transformative potential. Her scholarly interests span:

  • Intelligent computation and adaptive systems

  • Machine learning algorithms and applications

  • Real-time processing in embedded platforms

  • AI-driven smart education systems

Her work aims to make AI more accessible and functional within embedded environments, contributing to smarter and more efficient systems in fields such as robotics, automation, and smart learning platforms.

Through the integration of embedded technology and intelligent algorithms, Dr. Zhu’s research not only advances theoretical models but also impacts practical applications in real-world scenarios.

🏆 Accolades and Recognition

Dr. Zhu’s contributions to the academic and research community have been widely acknowledged:

  • She is a Senior Member of the Chinese Association for Artificial Intelligence (CAAI), a prestigious role that reflects her depth of expertise and ongoing contributions to the field.

  • She serves as a Member of the Smart Education Professional Committee in China, working on innovative intersections between AI and modern pedagogy.

  • Additionally, she is a member of the Chinese National Technical Committee on Information Technology Standardization, where she contributes to national-level strategic standards in emerging technologies.

These roles position her not only as a researcher but also as a national thought leader in AI and educational technology.

🌍 Impact and Influence

The impact of Dr. Zhu’s work is far-reaching. Her research has contributed to:

  • Enhancing intelligent learning systems, providing personalized education experiences

  • Improving the efficiency of embedded AI, essential for mobile and IoT applications

  • Shaping national policies and standards that will guide China’s digital infrastructure for years to come

Her influence extends through her students and collaborators, many of whom have gone on to build careers in research, technology, and education—spreading the effects of her mentorship far beyond the university.

🌟 Legacy and Future Contributions

Looking ahead, Dr. Zhu aims to further her research in human-centric AI systems, autonomous learning environments, and next-generation embedded solutions. She continues to mentor young researchers and lead collaborative initiatives both nationally and internationally.

Her legacy will be marked not only by her scholarly publications and technical contributions but also by her role in empowering young innovators to embrace AI and embedded systems for the betterment of society.

As AI and embedded technologies continue to reshape the digital world, Dr. Minling Zhu’s ongoing research and leadership ensure she remains at the forefront of innovation, guiding technological progress with intellectual rigor, visionary foresight, and social responsibility.

Publication Top Notes

ContributorsMinling Zhu; Zhixin Xu; Qi Zhang; Yonglin Liu; Dongbing Gu; Sendren Shengdong Xu
Journal: Expert Systems with Applications

Year: 2025

ContributorsMin‐Ling Zhu; Jia‐Hua Yuan; En Kong; Liang‐Liang Zhao; Li Xiao; Dong‐Bing Gu; Alexander Hošovský
Journal: International Journal of Intelligent Systems
Year: 2025

Multi-Scale Fusion Uncrewed Aerial Vehicle Detection Based on RT-DETR

ContributorsMinling Zhu; En Kong
Journal: Electronics
Year: 2024

Lilei Sun | Artificial intelligence | Best Researcher Award | 13305

Assist. Prof. Dr. Lilei Sun | Artificial intelligence | Best Researcher Award 

Assist. Prof. Dr. Lilei Sun, Guizhou Minzu University, China

Prof. Dr. Lilei Sun is an associate professor at Guizhou Minzu University, China, specializing in deep learning, image processing, pattern recognition, and medical image processing. He completed his B.E. in computer technology in 2016 and obtained his Ph.D. in software engineering in 2022, both from Guizhou University, Guiyang. Dr. Sun’s research focuses on incomplete multi-view clustering and has led to notable contributions in academic publications, including 10 articles in prestigious journals. He serves as an associate editor for the International Journal of Image and Graphics and is actively involved in collaborative research projects.

Profile

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🌱 Early Academic Pursuits

Prof. Dr. Lilei Sun began his academic journey with a solid foundation in computer technology. He received his B.E. degree in Computer Technology from Guizhou University in Guiyang, China, in 2016. This laid the groundwork for his advanced studies in software engineering, a field that has had a significant impact on his subsequent research. His academic excellence and curiosity drove him to pursue a Ph.D. in Software Engineering from Guizhou University, which he successfully completed in 2022. Throughout his academic journey, he consistently demonstrated a deep passion for understanding the intricacies of deep learning, image processing, and pattern recognition, as well as their transformative potential in medical image processing. 🌟

🔬 Professional Endeavors and Contributions

Since 2024, Prof. Dr. Sun has been serving as an Associate Professor at Guizhou Minzu University, where he has made notable strides in both teaching and research. In his role, he mentors students and collaborates with fellow researchers on projects that explore cutting-edge technologies in deep learning and image processing. His contributions to the academic community extend beyond the classroom as he actively participates in various consultancy projects and industry collaborations, applying his research to real-world problems. Dr. Sun’s work has led to practical innovations in the fields of medical image processing and pattern recognition, areas that are increasingly critical for advancing healthcare solutions globally. 🏥

💡 Research Focus

Prof. Dr. Sun’s research interests revolve around deep learning, image processing, pattern recognition, and medical image processing. One of his key areas of focus is incomplete multi-view clustering, a method that enables more accurate data analysis in scenarios where information is incomplete or fragmented. This has potential applications in various fields, including healthcare, where the integration of multi-source medical data can lead to better diagnostic models and more personalized treatments. Additionally, his work on medical image processing leverages machine learning techniques to enhance the quality and accuracy of medical imaging, providing practitioners with more reliable diagnostic tools. The potential to save lives and improve healthcare outcomes makes this research both significant and timely. 🔍

🏆 Accolades and Recognition

Prof. Dr. Sun has garnered recognition for his dedication to both teaching and research. He has published 10 academic articles, many of which have been featured in respected journals indexed by SCI and Scopus. He is also an associate editor of the International Journal of Image and Graphics, a prestigious journal that underscores his expertise in the field. His work has earned him an esteemed position in the academic community, further enhanced by his ongoing contributions to industry projects and collaborations. Through his research, Prof. Dr. Sun has received significant acknowledgment from his peers, with numerous invitations to speak at international conferences and collaborate with experts from various research institutes globally. 🌍

🌟 Impact and Influence

Prof. Dr. Sun’s work has had a profound impact on the fields of image processing and medical image processing. His research on deep learning and pattern recognition has contributed to advancements in the way medical data is processed, interpreted, and used in clinical decision-making. His innovations are poised to help healthcare providers access more accurate, timely, and comprehensive information, ultimately leading to improved patient outcomes. Furthermore, his involvement in professional memberships and editorial boards for various scientific journals has allowed him to influence the direction of research in his areas of expertise. 📊

💫 Legacy and Future Contributions

As Prof. Dr. Sun continues his research journey, his legacy is beginning to take shape through his groundbreaking contributions to deep learning and medical image processing. His passion for exploring innovative solutions to real-world challenges, particularly in healthcare, positions him as a leader in his field. In the coming years, Prof. Dr. Sun aims to push the boundaries of incomplete multi-view clustering, further developing techniques that can be applied across multiple domains, including medical diagnostics, artificial intelligence, and big data analytics. His commitment to excellence in research, teaching, and mentorship will continue to inspire future generations of students and researchers.

Publications Top Notes

Contributors: Lilei Sun; Wai Keung Wong; Yusen Fu; Jie Wen; Mu Li; Yuwu Lu; Lunke Fei
Journal: Pattern Recognition
Year: 2025
ContributorsLilei Sun; Jie Wen; Chengliang Liu; Lunke Fei; Lusi Li
Journal: Neural Networks
Year: 2023
Contributors: Lilei Sun; Jie Wen; Junqian Wang; Yong Zhao; Bob Zhang; Jian Wu; Yong Xu
Journal: CAAI Transactions on Intelligence Technology
Year: 2023

Lijun FU | Computer Science | Excellence in Innovation

Prof Dr. Lijun FU | Computer Science | Excellence in Innovation | 13293

Prof Dr. Lijun FU, Laboratory of Big Data and Artificial Intelligence Technology, Shandong University, China

Prof. Dr. Lijun Fu is a prominent researcher at the Laboratory of Big Data and Artificial Intelligence Technology at Shandong University, China. He specializes in knowledge discovery, multimodal data management, big data analysis, and intelligent systems. His research spans several areas, including smart education, smart medicine, and industry digitization. Dr. Fu has led and contributed to numerous high-impact projects funded by government and industry, and his work has resulted in various academic publications and patents in areas like AI, medical imaging, and knowledge mapping. His contributions have significantly advanced the fields of AI and big data technology.

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Nadeem Zaidkilani | Artificial Intelligence | Cutting-edge Technology Research Award | 13255

Mr. Nadeem Zaidkilani | Artificial Intelligence | Cutting-edge Technology Research Award 

Mr. Nadeem Zaidkilani, University Rovira i Virgili, Spain

Mr. Nadeem Zaidkilani is a Ph.D. student in the Doctoral Program in Computer Engineering and Mathematics at the University Rovira i Virgili (URV), Spain. His research focuses on computer vision and medical image analysis, leveraging deep learning techniques to enhance automated diagnostic systems. With a strong background in software engineering and artificial intelligence, he has extensive experience in text mining, natural language processing, and biomedical data analysis. His work integrates advanced machine learning methodologies with practical applications in healthcare and technology.

Profile

Scopus

🎓 Early Academic Pursuits

Nadeem Zaidkilani embarked on his academic journey with a passion for computer science, earning his Bachelor’s degree in Computer Science from Birzeit University in 2005. His thirst for knowledge and drive for excellence led him to further his studies, culminating in a Master’s degree in Software Engineering from the same institution in 2019. During his master’s, he honed his expertise in requirements engineering, business analysis, software design, and other fundamental software engineering disciplines. His thesis on Automatic Classification of Apps Reviews for Requirement Engineering provided significant insights into customer needs in healthcare applications, demonstrating his analytical skills and research prowess.

Currently, Nadeem is pursuing a Ph.D. at URV University in Spain, specializing in Computer Vision for Medical Image Analysis. His research integrates deep learning, image processing, and natural language processing (NLP) to develop advanced solutions in medical imaging.

💼 Professional Endeavors

Throughout his career, Nadeem has gained extensive experience in various roles, each contributing to his growth as a seasoned software engineer, researcher, and educator.

🌟 Part-Time Lecturer at Al-Zaytoonah University of Science and Technology (2024–Present)

Nadeem’s teaching portfolio includes courses in Python, C++, and Human-Computer Interaction (HCI). His expertise extends to Agile and Scrum methodologies, emphasizing collaborative design, iterative feedback, and efficient software development cycles. His commitment to academia ensures that students receive industry-relevant knowledge and skills.

🧪 Text Mining Engineer (2023–2024)

Nadeem played a pivotal role in a biomedical research project, leveraging text mining to streamline the classification of PubMed literature. His contributions included:

  • Text Classification: Implementing advanced algorithms to categorize biomedical research papers.
  • Entity Relationship Mapping: Utilizing INDRA to identify connections between biomedical entities.
  • Workflow Development: Designing a student-teacher analogy model, where experts guide AI-driven literature analysis.

🛠️ Senior Application Specialist at Paltel (2012–2022)

In this role, Nadeem managed and optimized multiple business applications, ensuring seamless integration and high efficiency. Key projects included:

  • Paltel Portal & Business Portal: Facilitating customer and corporate service management.
  • Electronic Document Management System (EDMS): Enhancing document storage and retrieval processes.
  • E-Pay System: Implementing a secure electronic payment solution.
  • Vendor Management System (VMS): Streamlining vendor relations.
  • Bulk and Pull SMS System: Enhancing customer communication strategies.

🏆 Senior Software Developer at Hulul Company (2009–2012)

Nadeem worked on network provisioning, donor management systems, and document archiving while implementing Agile methodologies. His experience with Struts 2, JEE, HTML, and JavaScript strengthened his software development capabilities.

📝 IT Manager at Ministry of Finance (2007–2009)

As an IT manager, he successfully led projects within budget and time constraints, ensuring efficient system analysis, testing, and quality assurance.

📚 Junior Java Developer & Software Engineer Roles (2005–2007)

Nadeem contributed to the Palestinian Authority Tax Administration Computer System (PATACS), implementing J2EE-based solutions. His training in Spain on J2EE and XML web services further solidified his technical foundation.

📚 Contributions and Research Focus

Nadeem’s research is centered on deep learning, computer vision, and medical image analysis. His ongoing Ph.D. work aims to revolutionize medical imaging techniques by integrating AI-driven models. His past research efforts in text mining for biomedical literature and software requirement engineering have also contributed valuable insights to their respective fields.

🏅 Accolades and Recognition

Nadeem’s expertise has earned him recognition across academic and professional spheres. His extensive contributions to biomedical text mining, software engineering, and AI-driven medical imaging have established him as an innovator in the field. He has collaborated with leading experts and institutions, further solidifying his influence in software development and research.

Publication Top Notes

Author: N., Zaidkilani, Nadeem, M.Á., García, Miguel Ángel, D.S., Puig, Domenec Savi

Journal: Neurocomputing, 

Year: 2025

 

 

Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award

Dr. Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award

Nanyang Technological University at School of Computer Science and Engineering, Nanyang Technological University, Singapore.

Mohamed R. Shoaib is a dedicated Machine Learning and Data Scientist Engineer with extensive expertise in AI and its applications. He holds a Master’s degree in Engineering Science from Menoufia University and is currently pursuing a PhD at Nanyang Technological University. Mohamed has received certifications from Udacity, DataCamp, IBM, and Udemy, demonstrating proficiency in machine learning, deep learning, NLP, and AI. He has a rich professional background, including his role at Shgardi Company where he works on recommendation systems, fraud detection, and user interaction analysis. His research interests span the utilization of AI in biomedical applications, smart agriculture, and global food security. Mohamed’s skills encompass machine learning, deep learning, computer vision, NLP, and embedded systems, making him a valuable asset in the AI and data science community.

Professional Profiles:

Education 🎓

Mohamed R. Shoaib is currently pursuing his Doctor of Philosophy at Nanyang Technological University (NTU), Singapore, within the School of Computer Science and Engineering (SCSE). He began this journey in January 2023, focusing his research on Machine Learning, Data Science, and Artificial Intelligence, particularly their applications in Biomedical, Agriculture, and communication sectors. Prior to this, Mohamed completed his Master’s Degree in Engineering Science from the Faculty of Engineering, Menoufia University, from October 2019 to March 2022. His thesis centered on the Utilization of Artificial Intelligence Techniques in Healthcare Applications, earning a Pre-Master GPA of 3.46/4. Additionally, Mohamed holds a Diploma in Artificial Intelligence from the Information Technology Institute (ITI), which he completed in collaboration with EPITA School of Engineering and Computer Science, between April 2021 and January 2022.

Professional Experience 💼

Mohamed R. Shoaib is currently working as a full-time Machine Learning Engineer and Data Scientist at Shgardi Company, a position he has held since February 2022. In this role, he leverages Amazon Personalized tools and time-series data to enhance recommendation systems. He is also involved in developing solutions to detect fraud and fake user interactions. Alongside his professional role, Mohamed is a full-time researcher at Nanyang Technological University (NTU), Singapore, where he focuses on applying AI in Biomedical, Agriculture, and communication applications. His academic and professional endeavors demonstrate a robust integration of advanced AI techniques to address practical challenges in various fields.

Research Interest 🔍

Mohamed R. Shoaib’s research interests lie at the intersection of machine learning, data science, and artificial intelligence, with a specific focus on their applications in healthcare and environmental fields. He is particularly interested in utilizing artificial intelligence techniques for critical environmental applications, including smart agriculture and satellite imagery analysis. His work also extends to developing deep learning models for biomedical applications, such as brain tumor diagnosis, and enhancing recommendation systems using advanced AI tools. Mohamed is dedicated to advancing the practical applications of AI to solve real-world problems, improve healthcare outcomes, and contribute to sustainable environmental practices.

Awards and Honors 🏆

Mohamed R. Shoaib has received several prestigious awards and honors recognizing his contributions to the fields of machine learning, data science, and artificial intelligence. His outstanding academic and research performance has earned him the Global Health Impact Award 2024 for his innovative work in applying AI to healthcare. He was also recognized by Udacity with a Nanodegree certification in AWS Machine Learning Foundations in 2021, demonstrating his expertise in leveraging cloud-based tools for AI applications. Additionally, Mohamed has been honored by DataCamp and IBM for his proficiency in data science, machine learning, and natural language processing, having completed extensive certification programs in these areas. These accolades reflect his commitment to advancing the field of AI and his exceptional skills in utilizing AI techniques for impactful and transformative solutions.

Research Skills 🧠

Mohamed R. Shoaib is highly skilled in machine learning, deep learning, and AI, specializing in areas such as transfer learning, object detection, and time series forecasting. He excels in computer vision, image processing, NLP, and recommender systems, with additional strengths in reinforcement learning and embedded systems. Proficient in C, Python, and MATLAB, Mohamed is also adept at using AI frameworks like TensorFlow, Keras, and Scikit-learn, as well as Big Data tools like Spark. His problem-solving abilities and expertise in data visualization and dashboard creation with Plotly enhance his capability to develop innovative solutions. Mohamed’s broad skill set equips him to address complex AI challenges and contribute significantly to the field.

Publications

  1. Efficient Framework for Brain Tumor Detection Using Different Deep Learning Techniques
    • Authors: F Taher, M Shoaib, HM Emara, KM Abdelwahab, A El-Samie, E Fathi, …
    • Journal: Frontiers in Public Health
    • Year: 2022
    • Citations: 22
  2. Deep convolutional neural networks for COVID‐19 automatic diagnosis
    • Authors: Emara HM, Shoaib MR, Elwekeil M, El‐Shafai W, Taha TE, …
    • Journal: Microscopy Research and Technique
    • Year: 2021
    • Citations: 22
  3. Hybrid classification structures for automatic COVID-19 detection
    • Authors: MR Shoaib, HM Emara, M Elwekeil, W El-Shafai, TE Taha, AS El-Fishawy, …
    • Journal: Journal of Ambient Intelligence and Humanized Computing
    • Year: 2022
    • Citations: 16
  4. Deepfakes, misinformation, and disinformation in the era of frontier AI, generative AI, and large AI models
    • Authors: MR Shoaib, Z Wang, MT Ahvanooey, J Zhao
    • Conference: 2023 International Conference on Computer and Applications (ICCA)
    • Year: 2023
    • Citations: 13
  5. Simultaneous super-resolution and classification of lung disease scans
    • Authors: HM Emara, MR Shoaib, W El-Shafai, M Elwekeil, EED Hemdan, …
    • Journal: Diagnostics
    • Year: 2023
    • Citations: 13
  6. Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques
    • Authors: MR Shoaib, MR Elshamy, TE Taha, AS El‐Fishawy, FE Abd El‐Samie
    • Journal: Concurrency and Computation: Practice and Experience
    • Year: 2022
    • Citations: 13
  7. Efficient brain tumor detection based on deep learning models
    • Authors: MR Shoaib, MR Elshamy, TE Taha, AS El-Fishawy, FE Abd El-Samie
    • Journal: Journal of Physics: Conference Series
    • Year: 2021
    • Citations: 13
  8. Automatic modulation classification with 2D transforms and convolutional neural network
    • Authors: HS Ghanem, MR Shoaib, S El‐Gazar, H Emara, W El‐Shafai, …
    • Journal: Transactions on Emerging Telecommunications Technologies
    • Year: 2022
    • Citations: 9
  9. Automated diagnosis of EEG abnormalities with different classification techniques
    • Authors: E Abdellatef, HM Emara, MR Shoaib, FE Ibrahim, M Elwekeil, W El-Shafai, …
    • Journal: Medical & Biological Engineering & Computing
    • Year: 2023
    • Citations: 4
  10. A survey on the applications of frontier AI, foundation models, and large language models to intelligent transportation systems
    • Authors: MR Shoaib, HM Emara, J Zhao
    • Conference: 2023 International Conference on Computer and Applications (ICCA)
    • Year: 2023
    • Citations: 4