Kwang-Ho Seok | Artificial Intelligence | Best Researcher Award

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Prof. Kwang-Ho Seok | Artificial Intelligence | Best Researcher Award

Professor | Global Cyber University | South Korea

Professor Kwang Ho Seok is a distinguished scholar specializing in Artificial Intelligence, Extended Reality, Robotics, and Data Analysis at the School of AI Convergence, Global Cyber University, South Korea. where he developed expertise in robotics, control systems, and intelligent algorithms. With over fifteen years of academic and research experience, he has collaborated with leading institutions such as ETRI and Hyundai Motor Company, spearheading projects in power system automation, AR/VR education, and smart device optimization. His research integrates AI, human-computer interaction, and digital learning environments to enhance immersive technologies and autonomous robotic systems. Widely published in SCIE and Scopus-indexed journals, he has authored 17 documents with 79 citations across 78 referencing papers. His work reflects a strong commitment to advancing intelligent, human-centered innovation in AI-driven technologies.

Profiles : Scopus | Google Sholar 

Featured Publications : 

Seok, K. H., Kim, Y. H., & Son, W. H. (2021). Using visual guides to reduce virtual reality sickness in first-person shooter games. JMIR Serious Games, 9(3). (Cited by 42)

Seok, K. H., & Kim, Y. S. (2015). An in-vehicle application providing system based on driver’s biodata. Journal of Sensors. Hindawi. (Cited by 36)

Seok, K. H., & Kim, Y. S. (2016). A state of the art of power transmission line maintenance robots. Journal of Electrical Engineering and Technology, 11(5), 1412–1422. (Cited by 48)

Seok, K. H., & Kim, Y. H. (2020). A virtual reality sickness reduction based on real-time individual characteristics. International Journal of Advanced Science and Technology, 29(3), 5999–6009. (Cited by 28)

Kianeh Kandi | Machine Learning | Academic Excellence Award

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Mrs Kianeh Kandi |  Machine Learning |  Academic Excellence Award

Academic Researcher at politechnic of dimadrid,  Spain

Mrs. Kianeh Kandi is an accomplished software developer and researcher with a robust foundation in computer science and a passion for innovative solutions. Currently pursuing her PhD in Software Systems and Computing at the Polytechnic University of Madrid, she has demonstrated expertise in machine learning, data mining, statistical methods, and high-impact research.

Profile:

📚 Education:

  • PhD Candidate in Software Systems and Computing, Polytechnic University of Madrid (2022 – Present)
  • Bachelor and Master of Industrial Management, Azad University of Arak (2004 – 2011)
  • Bachelor of IT (Networking), Arak University (2017 – 2019)

🎯 Research & Professional Experience:

  • Researcher, Polytechnic University of Madrid (2022 – Present):
    Conducting innovative research on credit card risk assessment using machine learning and developing neural network models to predict and mitigate financial risks.
  • Specialist, Dana Insurance Company (2018 – 2022):
    Managed market risk assessments and drove sales operations to enhance revenue growth.
  • Digital Marketing Expert, Aryanik Software Company (2016 – 2017):
    Improved user experience through website design and executed digital marketing strategies.
  • Project Planner, Sala Dairy Company (2010 – 2011):
    Boosted production efficiency and optimized processes using mathematical methods.

🧠 Research Contributions:

  • Thesis: Optimal Scheduling in a Milk Production Line Based on Mixed Integer Linear Programming
  • Publications:
    • Enhancing Performance of Credit Card Models Using LSTM Networks and XGBoost Algorithms
    • Evaluating Deep Convolutional Neural Networks and Support Vector Regression for Creditworthiness Prediction

💡 Key Skills:

  • Programming Languages: Python, Java, C++
  • Machine Learning Tools: TensorFlow, PyTorch
  • Technologies: Docker, large-scale data processing, Power BI, IBM SPSS
  • Research Expertise: Neural networks, statistical data analysis, synthetic data generation

Publication Top Notes:

  • “Optimal Scheduling in a Milk Production Line Based on Mixed Integer Linear Programming”

  • “Enhancing Performance of Credit Card Models by Utilizing LSTM Networks and XGBoost Algorithms”

  • “Evaluating the Performance of Deep Convolutional Neural Networks and Support Vector Regression for Creditworthiness Prediction in the Financial Sector”