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

Dinar Ajeng Kristiyanti | Data Mining | Best Researcher Award

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Dr. Dinar Ajeng Kristiyanti | Data Mining | Best Researcher Award

Dr. Dinar Ajeng Kristiyanti, Universitas Multimedia Nusantara, Indonesia

Dr. Dinar Ajeng Kristiyanti is a passionate Lecturer and Assistant Professor with over a decade of experience in computer science. She holds a Bachelor’s and Master’s in Computer Science from Sekolah Tinggi Manajemen dan Informatika Nusa Mandiri and is pursuing her PhD at Institut Pertanian Bogor 🎓. Her research focuses on Sentiment Analysis, Machine Learning, and Data Mining 💻. Dr. Kristiyanti has published 20 national and 8 international papers 📑, earning recognition as a top 10 author in the SINTA Index (2020-2022). She is also a recipient of several awards for her academic excellence 🏅.

Publication profile

Google Scholar

Educational Background 🎓

Dr. Dinar Ajeng Kristiyanti has a strong academic foundation in computer science. She earned her Bachelor of Information Systems from Sekolah Tinggi Manajemen dan Informatika Nusa Mandiri (2011-2012) with a GPA of 3.76 📘. She continued her studies at the same institution, completing her Master’s in Computer Science (2012-2014) with an impressive GPA of 3.88 🏅. Currently, Dr. Kristiyanti is pursuing her Doctorate in Computer Science at Institut Pertanian Bogor (2020-present), further advancing her expertise in the field of data science and machine learning 💻.

 

Work Experience 🏫

Dr. Dinar Ajeng Kristiyanti has extensive teaching experience across several prestigious institutions. Since 2010, she has been a Lecturer at Universitas Bina Sarana Informatika, where she contributes to the fields of computer science and informatics. From 2015 to 2021, she also served as a Lecturer at Universitas Nusa Mandiri, imparting her knowledge to future professionals. In 2014, Dr. Kristiyanti was a Guest Lecturer at Universitas Budi Luhur, further expanding her academic reach. Her diverse teaching roles reflect her dedication to educating and mentoring students across various institutions 📚👩‍🏫.

 

Award History and Personal Achievements 🏆

Dr. Dinar Ajeng Kristiyanti has been recognized for her academic excellence and contributions to research. She ranked in the Top Ten Authors in the SINTA Science and Technology Index (2020-2022) for her performance at Universitas Bina Sarana Informatika and Universitas Nusa Mandiri 📊. She has also won awards for Best Paper and Presenter at various national and international seminars 🌍. Additionally, Dr. Kristiyanti was honored as the Best Graduate of her Master’s in Computer Science program at STMIK Nusa Mandiri 🎓. Her achievements reflect her dedication and impact in the field of computer science.

 

Publication Top Notes

  • Comparison of SVM & Naïve Bayes algorithm for sentiment analysis (2018) 📊 – Cited by 80
  • Sentiment analysis of smartphone product reviews using SVM-based PSO (2016) 📱 – Cited by 55
  • Prediction of Indonesia presidential election results using Twitter sentiment analysis (2019) 🇮🇩 – Cited by 50
  • Feature selection for cosmetic product review using GA, PSO, and PCA (2017) 💄 – Cited by 45
  • Comparison of Naïve Bayes and SVM using PSO for e-wallet review (2020) 💳 – Cited by 39
  • Sentiment analysis for Halodoc app using Naïve Bayes, SVM, and KNN (2021) 🩺 – Cited by 34
  • Sentiment analysis of cosmetic reviews using SVM and PSO (2015) 💅 – Cited by 32
  • Machine Learning for Beginners (2022) 📖 – Cited by 29
  • E-wallet sentiment analysis using Naïve Bayes and SVM (2020) 💼 – Cited by 25
  • Sentiment analysis of cosmetic product review using feature selection comparison (2015) 👗 – Cited by 25
  • Decision support system for employee bonus using AHP at Buah Hati Ciputat Hospital (2018) 🏥 – Cited by 24
  • Decision support system for employee selection with profile matching analysis (2017) 🧑‍💼 – Cited by 20
  • Web-based thesis monitoring system for Mercu Buana University (2020) 💻 – Cited by 16
  • Application of seasonal multiplicative decomposition for inventory forecasting at PT. Agrinusa (2020) 📦 – Cited by 13
  • Sentiment analysis of public acceptance of COVID-19 vaccines in Indonesia (2023) 💉 – Cited by 11
  • Feature selection using v-shaped transfer function for salp swarm algorithm in sentiment analysis (2023) 🐟 – Cited by 11

Conclusion ✅

Dr. Dinar Ajeng Kristiyanti’s strong academic credentials, prolific research output, and numerous recognitions make her highly suitable for the Best Researcher Award. Her expertise in computer science, coupled with her dedication to innovation and teaching, align well with the award’s criteria, making her a strong candidate for this prestigious recognition.

 

 

 

Deepali Hirolikar | Machine Learning Award | Best Researcher Award

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Dr. Deepali Hirolikar | Machine Learning Award | Best Researcher Award

Dr. Deepali Hirolikar, PDEA,s College of Engineering, Manjari(Bk.), Pune, India

Dr. Deepali S. Hirolikar is the Head of the Department of Information Technology at PDEA’s College of Engineering, Pune, with 18 years of experience in academia. She holds a PhD in Information Technology from Shri JJT University, Rajasthan. Dr. Hirolikar has published numerous papers in national and international journals, focusing on topics such as IoT, cloud computing, and machine learning. She has also published a book on IoT security paradigms. As an active contributor to various workshops and conferences, she has received multiple accolades for her work. 🖥️📚🎓

Publication Profile

Orcid

Experience 🏆

Prof. Dr. Deepali S. Hirolikar has amassed over 18 years of experience in academia. She currently serves as the Head of the Information Technology Department and Assistant Professor at PDEA’s College of Engineering, Manjari, Pune, a position she has held since September 6, 2005. Before this, she was a Lecturer in the Computer Engineering Department at SRGSIOT, Hadapsar.

Education 📚

She completed her SSC at Keshavraj Vidyalaya, Latur in 1995 with distinction, and her HSC at Dayanand Science Junior College, Latur in 1997 with first class. She earned her Diploma in Computer Science Engineering from PLGP, Latur in 2000 with first class, and her BE in Computer Science and Engineering from Dr. BAMU, Aurangabad in 2004 with distinction. Prof. Dr. Hirolikar obtained her ME in Information Technology from UOP Pune, MIT College of Engineering, Pune in 2011 with first class, and her PhD in Information Technology from Shri JJT University, Rajasthan in 2021.

 

Research Focus

Deepali Hirolikar’s research primarily focuses on using metaheuristic methods and machine learning for efficiently predicting and classifying heart disease data. Her work includes the development and application of advanced algorithms to enhance the accuracy and efficiency of heart disease prediction models. By leveraging mathematical and engineering principles, she contributes to the field of medical data analysis, particularly in identifying patterns and improving diagnostic processes. Her research also spans the integration of machine learning techniques with medical datasets to facilitate better health outcomes.

Publication Top Notes

Metaheuristic Methods for Efficiently Predicting and Classifying Real Life Heart Disease Data Using Machine Learning