Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

Profile: Orcid

Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.

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

Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

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Mr. Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

lecturer The University Of Lahore Pakistan

Ali Raza is a passionate researcher, educator, and developer specializing in computer science. With a strong academic background and extensive experience in machine learning, deep learning, and computer vision, he has contributed significantly to cutting-edge research. Currently serving as a Lecturer at the University of Lahore, Ali has also worked as a Visiting Lecturer at KFUEIT and a Full Stack Python Developer in the software industry. His expertise lies in AI-driven solutions, research writing, and technological advancements in artificial intelligence.

Profile

Google Scholar

Education 🎓

  • MS Computer Science (2021-2023) | Khwaja Fareed University of Engineering and Information Technology (KFUEIT), CGPA: 3.93
  • BS Computer Science (2017-2021) | KFUEIT, CGPA: 3.47

Professional Experience 💼

  • Lecturer | University of Lahore (2024 – Present)
  • Visiting Lecturer | KFUEIT (2022 – 2023)
  • Full Stack Python Developer | BuiltinSoft Software Industry (2020 – 2021)

Research Interests 📈

Ali Raza’s research focuses on artificial intelligence, machine learning, deep learning, and computer vision. He is particularly interested in developing AI-driven solutions for medical imaging, agricultural applications, and energy consumption prediction. His contributions span multiple domains, showcasing his ability to integrate AI with real-world challenges.

Awards & Certifications 🏆

  • Best Researcher Award | ScienceFather (26/06/2024)
  • Use of Generative AI in Higher Education | Punjab Higher Education Commission
  • Machine Learning with Python (ML0101EN) | IBM Developer Skills Network

Publications Top Notes: 📚

Ali Raza has authored 61 research publications in reputed journals with high impact factors. Below are some of his recent publications:

“Novel Transfer Learning Approach for Hand Drawn Mathematical Geometric Shapes Classification” (2025) PeerJ Computer Science (IF: 3.8)

“Citrus Diseases Detection Using Innovative Deep Learning Approach and Hybrid Meta-Heuristic” (2025) PLOS ONE (IF: 2.9)

“Novel Deep Neural Network Architecture Fusion for Energy Consumption Prediction” (2025) PLOS ONE (IF: 2.9)

“Novel Transfer Learning Based Bone Fracture Detection Using Radiographic Images” (2025) BMC Medical Imaging (IF: 2.9)

“Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crops” (2025) Food Science & Nutrition (IF: 3.5)

“BdSentiLLM: A Novel LLM Approach to Sentiment Analysis of Product Reviews” (2024) IEEE Access (IF: 3.4)

“An Innovative Artificial Neural Network Model for Smart Crop Prediction” (2024) PeerJ Computer Science (IF: 3.8)

“Enhanced Interpretable Thyroid Disease Diagnosis Using Synthetic Oversampling and Machine Learning” (2024) BMC Medical Informatics (IF: 3.3)

“Diagnosing Epileptic Seizures Using EEG Data and Independent Components” (2024) Digital Health (IF: 3.7)

“A Novel Meta Learning Based Approach for Thyroid Syndrome Diagnosis” (2024) PLOS ONE (IF: 2.9)