Assoc. Prof. Dr. Hui Zhang | Artificial Intelligence | Industry Achievement Award

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Assoc. Prof. Dr. Hui Zhang | Artificial Intelligence | Industry Achievement Award

Assoc. Prof. Dr. Hui Zhang, Guizhou University of Finance and Economics, China

Hui Zhang is a distinguished senior engineer and associate professor at the School of Information, Guizhou University of Finance and Economics, China. With a Ph.D. in Computational Mathematics from Guizhou Normal University, Zhang has a diverse background spanning both academia and industry. His expertise ranges from computational mathematics to big data and cloud computing, having held prominent roles in R&D departments in China’s tech industry. Additionally, Zhang has served as a reviewer for prestigious journals, contributed to key projects, and holds multiple patents in data science and technology. His ongoing research and professional services make him a well-recognized expert in his field.

Professional Profile

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orcid

Summary of Suitability for the Research for Industry Achievement Award

Dr. Hui Zhang demonstrates a unique combination of academic and industrial achievements, making him a strong candidate for the Research for Industry Achievement Award. His background in computational mathematics and computer science, coupled with his experience in R&D and leadership roles in the big data industry, aligns well with the award’s focus on impactful industrial contributions.

🎓    Education

Hui Zhang completed his Bachelor’s in Information and Computational Science from Guizhou Normal University in 2010, alongside a Bachelor’s in English Education. He pursued a Master’s in Computational Mathematics at the same institution from 2010 to 2013. Hui continued his academic journey by earning a Ph.D. in Computational Mathematics, focusing on innovative computational methods. His educational experience highlights a strong foundation in both mathematics and interdisciplinary learning, setting the stage for his research and teaching career at Guizhou University of Finance and Economics.

💼   Experience

Hui Zhang’s career includes a range of positions in both academia and industry. After completing his master’s degree, he worked as a Business Manager at the Postal Savings Bank of China. He then transitioned to academic research while pursuing his Ph.D., contributing to key projects at the Guizhou Key Laboratory of Information and Computing Science. In industry, Zhang served as a Senior R&D Engineer and later General Manager of the R&D Department at Guizhou-Cloud Big Data Industry Development Co. Since 2022, he has been an associate professor at Guizhou University of Finance and Economics and a postdoctoral researcher, continuing to innovate in computer science and data science fields.

🏅  Awards and Honors

Hui Zhang’s numerous recognitions include being a Review Expert for multiple academic and governmental bodies, such as the Guizhou Provincial Department of Science and Technology. His technical contributions have earned him industry accolades, and he was invited to join the Big Data Expert Committee of the China Computer Federation. Zhang has also been honored as an Industrial Mentor in Guizhou Province and contributed as an expert reviewer for the prestigious Alexandria Engineering Journal. His expertise in computational mathematics and data science has made him a sought-after advisor and collaborator.

🌍  Research Focus

Hui Zhang’s research focuses on computational mathematics, big data, and information systems, particularly in developing algorithms and systems for data processing and analysis. He has worked extensively on Pulsar Data Processing, contributing to the design and implementation of comparative analysis and visualization systems. His research extends into numerical analysis, with a focus on finite element methods for solving complex mathematical problems. Zhang’s interdisciplinary approach combines theoretical mathematics with practical applications in data science, making significant advances in these fields.

📖 Publication Top Notes

  1. Generalized picture fuzzy Frank aggregation operators and their applications
  2. A second-order accurate and unconditionally energy stable numerical scheme for nonlinear sine-Gordon equation
  3. Asymmetrical interactions driven by strategic persistence effectively alleviate social dilemmas
  4. A Certificateless Verifiable Bilinear Pair-Free Conjunctive Keyword Search Encryption Scheme for IoMT
  5. Deformations and Extensions of Modified λ-Differential 3-Lie Algebras

Mr.Danish Javed | Data Science | Best Researcher Award

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Mr.Danish Javed | Data Science | Best Researcher Award

Mr.Danish Javed, Taylor’s University Lakeside Campus, Malaysia

Danish Khan is a Ph.D. scholar specializing in Data Science at Taylor’s University, Malaysia, where he is advancing research in natural language processing (NLP). With a strong academic background in Software Engineering from Bahria University, Islamabad, Danish has built expertise in Python, machine learning, and deep learning. He has held teaching roles as a Senior Lecturer at the University of Central Punjab, Lahore, and is currently a tutor at Taylor’s University, Malaysia. His career reflects a commitment to advancing computer science education, mentoring students, and leading post-graduate councils. Danish is also a prolific researcher, contributing to various data science and sentiment analysis projects, particularly in the analysis of social media content and NLP.

Professional Profile

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Summary of Suitability for the Research for Best Researcher Award

Danish Javed presents a strong candidacy for the Research for Best Researcher Award based on his significant academic and research contributions, particularly in the fields of data science, machine learning, and natural language processing (NLP). His ongoing PhD in Data Science from Taylor’s University demonstrates his deep commitment to advancing research, especially in topics like sentiment analysis, deep learning, and Twitter bot detection. Danish has published research in Scopus-indexed conferences and Q1/Q4 journals, which highlights the academic impact of his work.

🎓 Education 

Danish Khan has consistently pursued excellence in academia, currently working toward his Ph.D. in Data Science at Taylor’s University, Lakeside Campus, Malaysia. His doctoral research focuses on natural language processing (NLP), specifically exploring frameworks for sentiment analysis, bot detection, and text analytics. Prior to his Ph.D., Danish earned his M.S. in Software Engineering from Bahria University, Islamabad, Pakistan, where he delved into the intricacies of machine learning, image processing, and artificial intelligence. His academic foundation also includes a B.S. in Software Engineering from Bahria University, during which he developed strong programming skills in Python, Java, and C++, equipping him with the tools to tackle complex computational problems. His academic journey reflects his deep interest in understanding data structures and algorithms, making him proficient in implementing advanced analytics and programming solutions.

💼 Experience 

Danish Khan has a broad range of teaching and leadership experience, with over five years in academia. He is currently a Tutor at Taylor’s University, Malaysia, where he conducts tutorials in data science, supervises student projects, and plays a key role in shaping the post-graduate student experience. He previously served as the President of the Post-Graduate Student Council, organizing events and representing student perspectives in university meetings. Prior to his current role, Danish was a Senior Lecturer in the Faculty of Information Technology at the University of Central Punjab, Lahore, where he taught computer science and software engineering courses, supervised final-year projects, and contributed to extracurricular activities such as organizing sporting events. Additionally, Danish has experience as a QA Analyst at Orbit Institute of Technology in Lahore, where he maintained the quality standards in the Software Engineering department.

🏅Awards and Honors

Danish Khan has received notable recognition throughout his academic and professional career. His published research in data science and natural language processing has been featured in prominent Scopus-indexed conferences and reputed journals. As a Ph.D. scholar, he has been honored with several merit-based scholarships for academic excellence. Danish was also recognized for his leadership efforts while serving as President of the Post-Graduate Student Council at Taylor’s University, where he played a pivotal role in advocating for post-graduate student welfare. During his tenure at the University of Central Punjab, Lahore, he earned commendations for his outstanding contributions to teaching and student mentorship. Additionally, his development of an Android application, “Forex Profit Gain,” has garnered attention, earning placement in the Google Play Store. These accolades reflect his deep commitment to both academic rigor and innovative problem-solving in the field of data science.

🌐 Research Focus 

Danish Khan’s research is centered on data science, particularly natural language processing (NLP), machine learning, and sentiment analysis. His Ph.D. work at Taylor’s University, Malaysia, focuses on advanced techniques in deep learning to analyze and classify text-based data. His key areas of research include social media analytics, Twitter bot detection, and sentiment analysis of public opinion during crises such as the COVID-19 pandemic. Danish has contributed to frameworks that improve sentiment analysis by leveraging oversampling techniques and random minority oversampling, which enhance the accuracy of sentiment classification in user-generated content. His research also extends to explainable artificial intelligence (AI), where he has designed models for transparent and interpretable detection of bots on social media platforms. Danish’s academic pursuit aims to contribute practical, data-driven insights to solve real-world problems using cutting-edge AI and NLP technologies.

📖 publications Top Notes

“Framework for Improved Sentiment Analysis via Random Minority Oversampling for User Tweet Review Classification.”
Citation count: 25
“Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis.”
Citation count: 6
“Football Analytics for Goal Prediction to Assess Player Performance.”
Citation count: 4
“Explainable Twitter Bot Detection Model for Limited Features.”
Citation count: 2
“Explainable Machine Learning Based Model for Heart Disease Prediction.”
“Analyzing the Efficacy of Bot Detection Methods on Twitter/X.”