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

Belal Hamed | Computer Science | Best Researcher Award

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Dr. Belal Hamed | Computer Science | Best Researcher Award

Assistant Lecturer at Department of Computer Science, Faculty of Science, Minia University, Egypt

Belal Ahmed Mohammed Hamed is an Assistant Lecturer at the Department of Computer Science, Faculty of Science, Minia University, and at the Department of Artificial Intelligence, Minia National University, Egypt. He holds a master’s degree in Computer Science, with research expertise in bioinformatics, machine learning, and graph-based disease prediction models. His work focuses on developing advanced algorithms for pattern recognition in DNA sequences and medical data analysis. He has published in Scopus and SCI-indexed journals, and contributed to six research projects, including two funded ones. He also serves as a reviewer for journals such as Scientific Reports and The Journal of Supercomputing. His notable contributions include a high-accuracy Graph Convolutional Network model for Alzheimer’s gene prediction.

Profile:

Academic Background:

Belal holds a Master’s degree in Computer Science. His academic training and research work are rooted in computer science, with a focus on interdisciplinary applications in healthcare and genomics.

Research Areas:

  • Bioinformatics

  • Machine Learning

  • SNP-based Disease Prediction

  • Graph Neural Networks

  • DNA Pattern Matching Algorithms

Research Contributions:

Belal developed a deep learning model that integrates SNP data and Graph Convolutional Networks (GCNs) to predict gene-disease associations, specifically in Alzheimer’s disease. The model achieved 98.04% accuracy and AUROC of 0.996, identifying both known and novel genes. His framework is adaptable for use in other diseases, supporting personalized medicine and clinical research.

Publications & Impact:

  • 4 research papers in SCI/Scopus-indexed journals (Springer Nature, Wiley)

  • Google Scholar Citations: 73

  • h-index: 3

Research & Projects:

  • Participated in 6 research projects, including 2 funded

  • Contributed to 1 industry-academic collaboration in medical data analysis

Editorial Roles:

  • Reviewer for The Journal of Supercomputing, Scientific Reports, and Medical Data Mining Journal

  • Young Scientist – Medical Data Mining Journal

Collaborations:

Active in interdisciplinary research teams, particularly in genomics and artificial intelligence.

Publication Top Notes:

Yunfei Zì | Computer Science | Best Researcher Award

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Dr. Yunfei Zì | Computer Science | Best Researcher Award

Researcher Wuhan University of Technology China

Zi Yunfei is a distinguished researcher specializing in voiceprint recognition and artificial intelligence, affiliated with the Wuhan University of Technology. His expertise lies in developing advanced speaker verification systems and acoustic feature extraction methods, especially within IoT contexts. Currently, he is concluding his Ph.D. under the guidance of Professor Xiong Shengwu.

Profile

Orcid

Google scholar

Education 🎓

  • Ph.D. in Computer Science and Technology (2019–2023) – Wuhan University of Technology
  • M.Eng. in Information and Communication Engineering (2016–2019) – Beijing University of Graphic Arts
  • B.Eng. in Computer Science and Technology (2011–2015) – Northeast Petroleum University

Experience 💼

Zi has led and contributed to various research initiatives, including a Huawei NRE project and significant AI advancements in IoT voiceprint recognition and military voice monitoring. His technical contributions have been instrumental in enhancing acoustic feature extraction and system integration on Huawei’s deep learning platform, MindSpore.

Research Interests 🔍

  • Voiceprint Recognition
  • Short Utterance Speaker Verification
  • Artificial Intelligence & Deep Learning
  • Acoustic Feature Enhancement
  • IoT Smart Services

Awards 🏆

  • Outstanding Academic Achievement Award – Beijing University of Graphic Arts, 2018
  • Outstanding Master’s Thesis Award – Beijing University of Graphic Arts, 2019
  • Huawei Smart Base Future Star Award – Ministry of Education-Huawei, 2021
  • Outstanding Doctoral Thesis Award – Wuhan University of Technology, 2024

Publications Top Notes📚:

Multi-Fisher and Triple-Domain Feature Enhancement-Based Short Utterance Speaker Verification for IoT Smart Service – IEEE Internet of Things Journal (2024) [DOI:10.1109/JIOT.2023.3309659]

Joint Filter Combination-based Central Difference Feature Extraction – Expert Systems with Applications (2023) [DOI:10.1016/j.eswa.2023.120995]

Fisher Ratio-Based Multi-Domain Frame-Level Feature Aggregation – Engineering Applications of Artificial Intelligence (2024) [DOI:10.1016/j.engappai.2024.108063]

Short-Duration Speaker Verification by Joint Filter Superposition – IEEE Transactions on Consumer Electronics (2024) [DOI:10.1109/TCE.2024.3411116]

Aggregating Discriminative Embedding by Triple-Domain Feature Joint Learning – Biomedical Signal Processing and Control (2023) [DOI:10.1016/j.bspc.2023.104703]