Sara A. Shehab | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Sara A. Shehab | Artificial Intelligence | Best Researcher Award

Faculty Of Computer And Artificial Intelligence | Egypt

Assoc. Prof. Dr. Sara A. Shehab  is an Associate Professor in Computer Science at the University of Sadat City, Egypt, with expertise spanning artificial intelligence, bioinformatics, computational biology, quantum computing, and computer security. Her research focuses on developing intelligent algorithms for biological data analysis, optimization, and machine learning applications in medicine and environmental sustainability. She has contributed significantly to the advancement of multiple sequence alignment techniques, parallel and dynamic algorithms, and predictive modeling using machine learning. Her recent work explores deep learning for biomedical image analysis, explainable AI for green energy production, and hybrid optimization approaches for precision classification and prediction tasks. Dr. Shehab has published extensively in peer-reviewed international journals and conferences, collaborating with leading scholars in AI-driven bioinformatics and sustainable computing. She also serves as a reviewer for international journals and conferences, contributing to the academic community through quality evaluation and mentorship. Her professional experience includes leadership in e-learning, digital transformation, and program coordination within higher education, reflecting a strong integration of research, teaching, and institutional development. Through her interdisciplinary approach, she bridges artificial intelligence with biological and environmental sciences, fostering innovation in intelligent systems for healthcare, sustainability, and data-driven decision-making.

Profile: Google Scholar

Featured Publications

Shehab, S. A., Keshk, A., & Mahgoub, H. (2012). Fast dynamic algorithm for sequence alignment based on bioinformatics. International Journal of Computer Applications, 37(7), 54–61.

Ahmed, R. A. E. H., Shehab, S. A., Elzeki, O. M., & Darwish, A. (2024). An explainable AI for green hydrogen production: A deep learning regression model. International Journal of Hydrogen Energy, 83, 1226–1242.

Shehab, A. E. H. S., Mohammed, K. K., & Darwish, A. (2024). Deep learning and feature fusion-based lung sound recognition model to diagnose respiratory diseases. Soft Computing.

Shehab, A. E. H. S., & Darwish, A. (2023). Water quality classification model with small features and class imbalance based on fuzzy rough sets. Environment, Development and Sustainability.

Shehab, S., Shohdy, S., & Keshk, A. E. (2017). PoMSA: An efficient and precise position-based multiple sequence alignment technique. arXiv preprint arXiv:1708.01508.

Jinglin Li | Computer Science | Best Researcher Award

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Mr. Jinglin Li | Computer Science | Best Researcher Award

Engineer | China National Nuclear Corporation | China

Li Jinglin is a researcher specializing in intelligent systems, reinforcement learning, and energy-efficient technologies for industrial and service applications. He holds advanced degrees in Instrument Science and Technology, Electrical Engineering, and Vehicle Engineering with a focus on new energy systems. His research encompasses the development of intelligent interactive service technologies for elderly care, optimization of energy-harvesting wireless sensor networks, and multi-task scheduling for energy-secured unmanned vehicles. He has led projects on digital twin platform technologies and vertical displacement control of nuclear fusion plasma, applying deep reinforcement learning to enhance system performance and replace traditional control methods. Li has extensive experience in algorithm design, including MATLAB-based reinforcement learning, adaptive dynamic programming, and multi-level exploration deep Q-network scheduling, with applications in optimal microgrid transmission, mobile charging sequence scheduling, and network monitoring. His work has resulted in multiple first-author publications in high-impact journals covering reinforcement learning, wireless sensor networks, and energy management, as well as conference contributions in control and automation. Beyond his technical expertise, he demonstrates strong analytical, problem-solving, and team collaboration skills, with experience in summarizing complex research findings and implementing practical solutions. Li actively engages in academic presentations and has earned recognition for his research achievements. In addition to his research, he maintains leadership roles in university sports teams, reflecting his commitment to teamwork, discipline, and resilience. His professional approach combines a proactive mindset, logical thinking, and a dedication to advancing intelligent and sustainable technological solutions across both industrial and service domains.

Profile: Scopus

Featured Publications

Li, J. (2024). A deep reinforcement learning approach for online mobile charging scheduling with optimal quality of sensing coverage in wireless rechargeable sensor networks. Ad Hoc Networks, 156, 103431.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks. Journal of Ambient Intelligence and Humanized Computing, 15(6), 2869–2881.

Li, J. (2023). Mobile charging sequence scheduling for optimal sensing coverage in wireless rechargeable sensor networks. Applied Sciences, 13(5), 2840.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. IEEE Transactions on Network and Service Management.

Li, J. (2024). A swarm deep reinforcement learning based on-demand mobile charging-scheduling and charging-time control joint algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. Expert Systems with Applications.

Sheng-Chieh Lu | Machine learning | Best Researcher Award

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Dr. Sheng-Chieh Lu | Machine learning | Best Researcher Award

University of Texas MD Anderson Cancer Center | United States

Dr. Sheng-Chieh Lu is a data-driven nursing scientist and healthcare informatics expert, currently serving as a Data Scientist in the Department of Symptom Research at The University of Texas MD Anderson Cancer Center. He earned his BS in Nursing and MS in Medical Informatics from National Yang-Ming University, Taiwan, and completed his PhD in Nursing at the University of Minnesota in 2020. His doctoral research focused on the evaluation of integrative health interventions using data science approaches.

Profile:

Educational Background:

Dr. Sheng-Chieh Lu earned his Bachelor of Science in Nursing and Master of Science in Medical Informatics from National Yang-Ming University in Taiwan. He completed his PhD in Nursing at the University of Minnesota in 2020 under the mentorship of Dr. Karen A. Monsen and Dr. Connie White Delaney. His dissertation focused on data-driven evaluation of integrative health interventions in community-based care.

Professional Licenses and Certifications:

Dr. Lu is a Registered Nurse (Taiwan) and holds certifications including the Primary Certificate of Informatics Nurse and Primary Emergency Medical Technician.

Academic and Professional Positions:

Dr. Lu currently serves as a Data Scientist at the MD Anderson Cancer Center, where he previously held positions as a Postdoctoral Fellow and Computational Scientist. He is also an Affiliate Faculty Member at the University of Minnesota School of Nursing. His past roles include Nursing Informatics Specialist at En Chu Kong Hospital and Adjunction Lecturer at Yuanpei University of Medical Technology in Taiwan.

In 2023, he was appointed Review Editor for Frontiers in Digital Health, reflecting his active role in academic publishing and peer review.

Research Interests and Contributions:

Dr. Lu’s research integrates nursing, informatics, data science, and machine learning to enhance healthcare delivery and outcomes. His contributions span topics such as cancer symptom management, immunotherapy toxicity prediction, robotic bronchoscopy diagnostics, and the application of large language models (LLMs) in patient-reported outcome measurement.

He has served in multiple roles, including principal investigator, data scientist, and collaborator on diverse projects involving electronic health records (EHRs), clinical decision support systems, and mHealth applications. His dissertation and subsequent studies have significantly contributed to the advancement of integrative and community-based care models.

Memberships and Editorial Service:

Dr. Lu is a member of the American Medical Informatics Association, Midwest Nursing Research Society, Taiwan Nurses Association, and Taiwan Nursing Informatics Association. He is a Review Editor for Frontiers in Digital Health and previously served as Student and Adjunction Co-director of the Omaha System Partnership.

Honors and Scholarships:

Dr. Lu has been recognized with several prestigious fellowships and scholarships, including:

  • Marilee A. Miller Fellowship in Educational Leadership (2017–2018)

  • Connie White Delaney Fellowship in Nursing Innovation (2017–2018)

  • Beatrice L. Witt Endowment Fund (2016–2017)

  • Violet A. Shea Nursing Scholarship (2016–2017)

  • Council of Agriculture Scholarship (2007–2011)

Citation Metrics:

  • Total Citations: 575

  • Citations Since 2020: 569

  • h-index: 14

  • i10-index: 19

Publication Top Notes:

Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison
2022
Citations: 75

On the importance of Interpretable Machine Learning Predictions to Inform Clinical Decision Making in Oncology
2023
Citations: 69

Machine learning–based short-term mortality prediction models for patients with cancer using electronic health record data: systematic review and critical appraisal
2022
Citations: 42

Novel machine learning approach for the prediction of hernia recurrence, surgical complication, and 30-day readmission after abdominal wall reconstruction
2022
Citations: 39

Using ADDIE model to develop a nursing information system training program for new graduate nurse
2016
Citations: 39

Kehinde Akinwolere | Artificial intelligence | Best Researcher Award

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Mr. Kehinde Akinwolere | Artificial intelligence | Best Researcher Award

Ball State University, United States

Mr. Kehinde Akinwolere is a corporate lawyer and interdisciplinary researcher currently pursuing a Master’s degree in Information and Communication Sciences at Ball State University, USA, where he maintains a perfect GPA of 4.0. With a background in law (LL.B, Obafemi Awolowo University; BL, Nigerian Law School), he brings over six years of professional experience in legal advisory, corporate governance, and regulatory compliance.

Profile:

🎓 Academic Excellence:

  • 🎯 GPA: 4.0

  • 🧠 Currently pursuing a Master’s in Information and Communication Sciences at Ball State University (2024–2026)

  • ⚖️ Bachelor of Laws (LL.B) from Obafemi Awolowo University

  • 🎓 Licentiate Degree in Law (BL) from Nigerian Law School

🧩 Professional Experience Highlights:

  • 📢 Graduate Teaching Assistant – Ball State University (2024–Present)

  • ⚖️ Pre-Legal Counsel – A.P. Moller – Maersk, West Africa (2023–2024)

  • 🏛️ Corporate Governance Consultant – DCSL (formerly Deloitte Corporate Services Ltd) (2018–2023)

  • 🗣️ Corporate Communications Lead – DCSL (2018)

  • ⚖️ Legal Associate – Iyiola, Oyedepo & Co (2018)

📚 Research & Publication:

  • 🧾 MDPI Publication (2024):
    “Corporate Governance and Information Systems in a Data-Driven World”
    🔗 Read Article

💼 Core Skills:

  • 📊 Corporate Law & Governance

  • 📄 Legal Drafting & Research

  • 🔍 Risk Identification & Policy Development

  • 🗣️ Communication & Negotiation

  • 🧪 Data Analysis & Regulatory Strategy

🏅 Notable Attributes:

  • 🌐 Interdisciplinary thinker in law, technology, and communication

  • 🏆 Recognized for practical impact in legal consulting and governance reform

  • 📈 Strong academic and research promise in a data-driven regulatory landscape

Publication:

  • Text Classification: How Machine Learning Is Revolutionizing Text Categorization

Rui Miao | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Rui Miao |  Artificial Intelligence | Best Researcher Award

Associate Researcher at Zhejiang Lab, China

Dr. Rui Miao is an Associate Researcher at Zhejiang Lab, specializing in artificial intelligence and image processing. He earned his Ph.D. in Engineering from Beihang University in 2022 and began postdoctoral research the same year, focusing on multi-modal cross-domain image enhancement for intelligent navigation systems across air, land, and water. Dr. Miao has led or participated in 7 research projects, published in top-tier journals such as IEEE Transactions on Geoscience and Remote Sensing and Pattern Recognition, and holds 28 patents (published or pending). His work contributes significantly to intelligent visual systems and applied AI.

Profile:

👨‍🎓 Academic Background

Dr. Rui Miao earned his Ph.D. in Engineering from Beihang University in 2022. He is currently an Associate Researcher at Zhejiang Lab, China.

🧠 Research Focus

His work explores cutting-edge areas such as:

  • Multimodal Image Processing

  • AI-based Image Generation & Matching

  • Visual Enhancement for Intelligent Systems

  • Model Inference Acceleration

🧪 Research Contributions

Dr. Miao has contributed to 7 major research projects and published impactful papers in top-tier journals like:

  • IEEE Transactions on Geoscience and Remote Sensing (TGRS)

  • Pattern Recognition (PR)

🔬 Innovations & Patents:

He has filed or published 28 patents, focusing on advanced image enhancement algorithms tailored for cross-domain AI perception systems used in air, land, and water navigation.

📚 Publications & Recognition:

While still early in his academic journey, Rui’s innovative work has already gained visibility in the scientific community, although citation metrics and editorial roles are still forthcoming.

Publication:

“Attention-Guided Progressive Frequency-Decoupled Network for Pan-Sharpening”
IEEE Transactions on Geoscience and Remote Sensing, 2024.
DOI: 10.1109/TGRS.2024.3376730
Authors: Rui Miao, Hang Shi, Fengguang Peng, Siyu Zhang

 

 

Xiang Ma | Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Xiang Ma | Computer Science and Artificial Intelligence | Best Researcher Award

Postgraduate sichuan unviersity China

📖 Xiang Ma is a student at Sichuan University specializing in Electronic Information and Control Engineering. His research focuses on developing innovative solutions for image super-resolution reconstruction in construction site scenarios. By leveraging computer vision, machine learning, and engineering principles, Xiang’s work aims to improve image quality, safety, and monitoring efficiency in real-world construction environments.

Profile

Orcid

Education

🎓 Xiang Ma is pursuing a degree in Electronic Information and Control Engineering at Sichuan University. With a strong academic foundation, he integrates principles of electronic systems, computer vision, and machine learning in his research.

Experience

🔧 Xiang Ma has contributed to three completed and ongoing research projects, including collaborations with CSCEC First Bureau Technology R&D Program and the Sichuan Province Major Special Project on Intelligent Manufacturing and Robotics. His work bridges academic research with industrial applications in safety and automation technologies for construction sites.

Research Interest

🔍 Xiang Ma is passionate about Image Super-Resolution Reconstruction, with a focus on enhancing low-resolution images affected by noise in construction scenarios. His research includes proposing the Lightweight Feature Enhancement Network (LFEN) to improve visual perception, edge detection, and noise immunity using advanced machine learning techniques.

Awards

🏆 Xiang Ma is applying for the Best Researcher Award for his contributions to image processing technologies in construction scenarios. His work has been recognized for its innovative approach to leveraging lightweight network designs for practical applications.

Publications Top Notes: 

📚 Xiang Ma has published three research papers in prestigious journals:

Liu, Y., Ma, X. & Cheng, J. (2024). Lightweight Feature Enhancement Network for Image Super-Resolution Reconstruction at Construction Sites. Arab Journal of Science and Engineering. Published Year: 2024. Cited by: 15 articles.

 

Quanzeng Liu | Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Quanzeng Liu | Computer Science and Artificial Intelligence | Best Researcher Award

Member Chinese Association of Automation China

Quanzeng Liu is a dedicated researcher and a CPC member, specializing in intelligent robot technology. Currently holding a Master’s degree in Control Science and Engineering from Anhui University of Technology, he has actively contributed to meta-heuristic algorithms, robot control, and path planning. With five research publications and numerous awards in academic competitions, Quanzeng’s work advances innovative solutions in robotics and automation systems.

Profile

Orcid

Education 🎓

Quanzeng Liu holds a Master’s degree in Control Science and Engineering from Anhui University of Technology, where his focus was on intelligent robot technology. His academic training has provided a robust foundation in control systems and advanced robotics, enabling significant contributions to both theory and practical applications.

Experience 💼

Quanzeng Liu has valuable research experience, participating in three major scientific research projects, including the collaborative innovation project of Anhui Province (GXXT-2023-068) and chairing the postgraduate innovation fund project (2023CX2086) at Anhui University of Technology. His research engagements reflect a strong capability in designing and improving robotic systems, particularly for multi-machine cooperative operations.

Research Interests 🔍

Quanzeng Liu’s primary research areas include meta-heuristic algorithms, robot control, and path planning. His work focuses on improving the performance of intelligent robots, including quadruped robots and weeding robots, as well as optimizing algorithms for visual SLAM and real-world robotic applications.

Awards 🏆

Quanzeng Liu has received five awards in prestigious academic competitions, showcasing his excellence in research and innovative problem-solving. These recognitions underscore his ability to translate complex theories into impactful solutions in robotics and automation.

Publications Top Notes:📚

Quanzeng Liu has published five influential papers in recognized journals and conferences, contributing to advancements in robotics and algorithms.

CMGWO: Grey wolf optimizer for fusion cell-like P systems
Heliyon, 2024. Read here

An Evaluation System for Multi-Machine Cooperative Operation of Weeding Robots Based on Fuzzy Combination Weight
China Automation Congress (CAC), 2024.

Robust visual SLAM algorithm based on target detection and clustering in dynamic scenarios
Frontiers in Neurorobotics, 2024. Read here

A hypergraph cell membrane computing network model for soybean disease identification
Scientific Reports, 2024. Read here

Conclusion ✅

Quanzeng Liu is an exceptional researcher whose work in robotics and intelligent systems contributes to solving complex challenges in automation and control. His innovative approach to meta-heuristic algorithms and robot path planning makes him a highly deserving candidate for the Best Researcher Award. With continued focus on industrial applications and broader collaborations, Quanzeng is poised to make even greater impacts in the future of robotics and automation.