Hong Wang | Artificial Intelligence | Best Researcher Award

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Prof. Hong Wang | Artificial Intelligence | Best Researcher Award

Prof. Hong Wang, Shandong Normal University, China

Prof. Wang earned his Ph.D. in Computer Science from the Chinese Academy of Sciences. His research focuses on Artificial Intelligence, Machine Learning, Healthcare Big Data, and Bioinformatics. 🧠 He has extensive teaching experience, with roles from Lecturer to Doctoral Supervisor. He has received multiple honors, including the Outstanding Graduate Tutor award and Shandong Province Science and Technology Progress prizes. 🏆 Prof. Wang has published widely, including papers on molecular property prediction and drug interactions. His current research includes cutting-edge AI applications in health. 💻

 

Publication Profile

Google Scholar

Education Background 🎓

Prof. Hong Wang completed his PhD in Computer Science from the Chinese Academy of Sciences in Beijing, China, from 1999 to 2002. Prior to that, he earned a Master of Science in Computer Science from Tianjin University in Tianjin, China, between 1988 and 1991. His academic journey began at Tianjin University, where he obtained his Bachelor of Science in Computer Science in 1988. His strong educational foundation has supported his exceptional career in AI, machine learning, and bioinformatics. 📚💻

 

Working Experience 👨‍🏫

Prof. Hong Wang has had a distinguished academic career at Shandong Normal University, starting as a Teaching Assistant from 1991 to 1995. He then served as a Lecturer from 1995 to 2000 and quickly advanced to the position of Associate Professor from 2000 to 2006. Since 2006, he has held the prestigious title of Professor, contributing significantly to the university’s academic growth. In 2009, Prof. Wang also became a Doctoral Supervisor, guiding the next generation of scholars and researchers. His career spans over three decades, focusing on teaching, research, and mentorship. 🎓📚👨‍🔬

 

Honors and Awards 🏅

Prof. Hong Wang has received numerous prestigious honors throughout his career, reflecting his dedication and contributions to academia. In March 2021, he was recognized as a March 8th Red Banner Holder. He was named Outstanding Graduate Tutor in September 2021 for his exceptional mentoring. In March 2019, he received the award for Outstanding Contribution to Achievement. His excellence in teaching was acknowledged with the University-Level Distinguished Teacher award in December 2014, followed by the Individual with Excellence in Teacher Ethics award in September 2014. Additionally, he was honored as a Good Teacher and Friend to College Students in January 2003. 🌟🎓👨‍🏫

 

Research Experience and Achievements 🔬

Prof. Hong Wang has led impactful research projects, including funding from the National Natural Science Foundation of China, with programs spanning from 2021 to 2024 (62072290) and 2017 to 2020 (61672329). He is also part of the Jinan City Science and Technology Bureau project from 2023 to 2024 (202228110). His outstanding contributions have earned him several prestigious awards, such as the Shandong Computer Society Science and Technology Progress Second Prize (First Place) in July 2024. Additionally, he received the Shandong Province Science and Technology Progress First Prize (7th place) in December 2022 and the Shandong Province Higher Education Outstanding Research Achievements Second Prize (First Place) in both 2020 and 2018. 🏆📚

 

Publication Top Notes

  • EDDINet: Enhancing drug-drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning – 2024
  • EMPPNet: Enhancing Molecular Property Prediction via Cross-modal Information Flow and Hierarchical Attention – Cited by 3, 2023
  • GCNs–FSMI: EEG recognition of mental illness based on fine-grained signal features and graph mutual information maximization – Cited by 8, 2023
  • Detecting depression tendency with multimodal features – Cited by 9, 2023
  • A Soft-Attention Guidance Stacked Neural Network for neoadjuvant chemotherapy’s pathological response diagnosis using breast dynamic contrast-enhanced MRI – Cited by 1, 2023
  • Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reactions – Cited by 6, 2023
  • Predicting drug-drug adverse reactions via multi-view graph contrastive representation model – Cited by 11, 2023
  • Explainable knowledge integrated sequence model for detecting fake online reviews – Cited by 9, 2023
  • CasANGCL: Pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction – Cited by 19, 2023
  • Dual network contrastive learning for predicting microbe-disease associations – Cited by 2, 2022
  • Knowledge graph construction for computer networking course group in secondary vocational school based on multi-source heterogeneous data – Cited by 2, 2022
  • Test Paper Generation Based on Improved Genetic Simulated Annealing Algorithm – 2022
  • MS-ADR: Predicting drug–drug adverse reactions based on multi-source heterogeneous convolutional signed network – Cited by 6, 2022
  • Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events – Cited by 1, 2022

Dharmapuri Siri | Deep Learning | Best Researcher Award

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Dharmapuri Siri | Deep Learning | Best Researcher Award

Assistant Prof Dharmapuri Siri, Gokaraju Rangaraju Institute of Engineering and Technology, india.

Dr. D. Siri is a dedicated academic professional with over 11 years of experience in teaching and research in the field of Computer Science and Engineering. She has served as an Assistant Professor in various prestigious institutions, including TRR Engineering College and Malla Reddy Engineering College for Women. Her work is focused on the intersection of software quality, machine learning, and data analysis.

Profile:

orcid

Education📚:

Dr. D. Siri holds a Ph.D. in Computer Science and Engineering from JJT University, obtained in 2022. She completed her M.Tech in Computer Science and Engineering from JNTU Hyderabad in 2010, and her B.Tech in Information Technology from the same university in 2006.

Professional Experience👩‍🏫:

Dr. Siri began her teaching career in 2008 as an Assistant Professor in the Department of Information Technology at TRR Engineering College, where she worked until 2013. She then transitioned to the Department of Computer Science and Engineering at TRR College of Engineering, continuing her role until 2017. From 2017 to 2019, she served as an Assistant Professor at Malla Reddy Engineering College for Women. Throughout her career, she has gained extensive experience in teaching core subjects like C, C++, Java, DBMS, Software Testing Methodologies, and Software Engineering.

Research Interests🔍:

Dr. Siri’s research interests are primarily focused on software quality improvement, bug prediction using machine learning techniques, and the application of deep learning methods to various domains such as sentiment analysis, medical imaging, and automated systems. Her work in these areas has been presented at numerous national and international conferences.

Awards and Recognitions🏆:

Dr. Siri’s innovation and contributions to the field have been recognized through a published patent titled “A Vehicle with Smart Biometric Device” (Application No. 201841019142-A), which was published in The Patent Office Journal No. 22/2018 on June 1, 2018.

Research Contributions 📚:

Dr. Siri’s research is primarily centered around software quality, machine learning, and data analysis. Her notable works include studies on bug prediction models, software engineering methodologies, and the application of machine learning techniques in software quality improvement. Her Ph.D. thesis on “Bug Prediction Model for Software Quality Using Machine Learning Techniques” reflects her deep commitment to enhancing software reliability and performance.

Publication:

Publications 📝:
Dr. Siri has published several research articles in reputed journals. Here are some of her notable publications:

  1. A Study on Bug Prediction in Determining The Software Quality | History Research Journal | 2019.
  2. Machine Learning Techniques on Historical Software Bugs for Prediction of Software Bugs | Think India Journal | 2019.
  3. Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis | IEEE Access | 2024.
  4. Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism | IEEE Access | 2024.