Ling Zheng | Computer Science and Artificial Intelligence | Research Excellence Award

Dr. Ling Zheng | Computer Science and Artificial Intelligence | Research Excellence Award

Fujian Maternity and Children Health Hospital | China 

The researcher holds advanced training in computer science with a strong specialization in artificial intelligence and machine learning, Computer Science and Artificial Intelligence particularly in efficient attention mechanisms and multimodal large models for medical and healthcare applications. Current research focuses on the development of intelligent systems for placental pathology analysis, automated diagnostic report generation, and AI-assisted clinical decision support. Major contributions include the construction of annotated medical image databases, AI-guided lesion standardization frameworks, and the application of dynamic intelligent models to support maternal health interventions. The research portfolio also extends to large-scale data management, privacy protection, and secure data sharing technologies for auditory and visual cognitive models, as well as knowledge graph representation and swarm intelligence collaboration. In addition to academic research, the work includes close collaboration with industry partners to develop domain-specific large models for gynecologic oncology and multimodal AI systems for placental pathology diagnosis. Scholarly contributions span high-impact peer-reviewed journals in artificial intelligence, medical informatics, data science, and interdisciplinary computational research. The researcher has also contributed to the academic community through service on program committees for leading international conferences in artificial intelligence, computer vision, and data analytics. Overall, the research demonstrates strong innovation, translational impact, and commitment to advancing AI-driven healthcare technologies.

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Featured Publications

Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone
A. Rampun, L. Zheng, P. Malcolm, B. Tiddeman, R. Zwiggelaar – Physics in Medicine & Biology, 61(13), 4796, 2016

Self-adjusting harmony search-based feature selection
L. Zheng, R. Diao, Q. Shen – Soft Computing, 19(6), 1567–1579, 2015

Feature grouping and selection: A graph-based approach
L. Zheng, F. Chao, N. Mac Parthaláin, D. Zhang, Q. Shen – Information Sciences, 546, 1256–1272, 2021

Boundary-aware network with two-stage partial decoders for salient object detection in remote sensing images
Q. Zheng, L. Zheng, Y. Bai, H. Liu, J. Deng, Y. Li – IEEE Transactions on Geoscience and Remote Sensing, 61, 1–13, 2023

A distributed joint extraction framework for sedimentological entities and relations with federated learning
T. Wang, L. Zheng, H. Lv, C. Zhou, Y. Shen, Q. Qiu, Y. Li, P. Li, G. Wang – Expert Systems with Applications, 213, 119216

Dr. Ajay K. Palit – Computational Intelligence – Best Researcher Award

Dr. Ajay K. Palit - Computational Intelligence - Best Researcher Award

University of Bremen - Germany

Professional Profile 

SCOPUS

EARLY ACADEMIC PURSUITS 🎓

Ajay K. Palit and Dobrivoje Popovic, distinguished scholars in the field of computational intelligence, began their academic journeys with a robust foundation in engineering and mathematics. Their early work laid the groundwork for their future contributions to the field of time series forecasting and industrial process control. Both scholars pursued advanced studies and research in their respective areas, developing a deep understanding of computational techniques and their applications in real-world scenarios.

PROFESSIONAL ENDEAVORS 🏢

Dr. Ajay K. Palit and Dr. Dobrivoje Popovic are both affiliated with the University of Bremen, Germany, where they continue to drive innovation in computational intelligence. Their professional careers are marked by significant roles in academia and industry, focusing on enhancing the effectiveness of time series forecasting through advanced computational techniques. Their expertise extends to the practical applications of these techniques in industrial systems and process control.

CONTRIBUTIONS AND RESEARCH FOCUS 🔬

Palit and Popovic have made notable contributions to the field of computational intelligence, particularly in time series forecasting. Their research encompasses a range of intelligent technologies, including neural networks, fuzzy logic, and evolutionary computation. They have pioneered hybrid computational approaches, such as neuro-fuzzy and transparent fuzzy/neuro-fuzzy modeling, which offer improved quality, Computational Intelligence model building, and predictive control in industrial processes. Their work addresses the challenges of on-line application and computational efficiency in industrial settings.

ACCREDITATIONS AND RECOGNITION 🏅

Their pioneering work has been widely recognized and has earned them respect in the academic and industrial communities. The book "Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications" by Palit and Popovic is acclaimed for its Computational Intelligence comprehensive exploration of intelligent technologies and their practical applications. The authors' contributions are integral to the ongoing development and refinement of computational tools used in industrial process control and forecasting.

IMPACT AND INFLUENCE 🌍

The influence of Palit and Popovic’s work extends across various industries, including manufacturing, process control, and research. By advancing computational intelligence techniques, they have significantly improved the ability to forecast and control industrial processes. Their research has helped bridge the gap between theoretical knowledge and practical application, making a tangible impact on industrial efficiency Computational Intelligence and predictive accuracy.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Ajay K. Palit and Dobrivoje Popovic continue to inspire and lead in their field, shaping the future of computational intelligence in time series forecasting. Their legacy is reflected in their innovative approaches and the ongoing relevance of their research in addressing complex industrial challenges. As they advance their work, they remain committed to exploring new computational techniques and applications, ensuring their contributions will have a lasting impact on the field.

NOTABLE PUBLICATIONS

Distributed RLGC transient model of coupled interconnects in DSM chips for crosstalk noise simulation 2008(8)