Pengfei Ji | Artificial Neural Networks | Best Researcher Award

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Dr. Pengfei Ji | Artificial Neural Networks | Best Researcher Award

Chief Technician at Cancer Hospital of Dalian University of Technology, Shenyang, China

Dr. Pengfei Ji is currently serving as the Chief Technician at the Cancer Hospital of Dalian University of Technology (also referred to as Liaoning Cancer Hospital). With a strong background in medical sciences and radiotherapy, Dr. Ji has focused his professional career on advancing cancer diagnosis and treatment, particularly through the integration of Artificial Intelligence (AI) technologies. His research includes AI-based methods for tumor diagnosis, personalized treatment planning, and prognosis prediction, contributing significantly to the development of precision oncology in China.

Publication Profile 

Scopus

Educational Background 🎓

  • Institution: Dalian Medical University, China

  • Degree: Master’s Degree in Medical Sciences

  • Specialization: Cancer Radiotherapy and AI Applications in Oncology

Professional Experience 💼

  • Current Designation: Chief Technician

  • Institution: Cancer Hospital of Dalian University of Technology / Liaoning Cancer Institute

  • Key Roles:

    • Oversight of technical operations in cancer radiotherapy

    • Implementation and optimization of AI-assisted diagnostic protocols

    • Research and development in personalized cancer therapy systems

    • Supervision of collaborative clinical studies and trials

Research Interests 🔬

  • Artificial Intelligence in Medical Imaging and Oncology

  • Tumor Diagnosis and Prognosis Prediction

  • Radiotherapy Techniques and Planning

  • Predictive Modeling and Clinical Decision Support Systems

  • AI-based Clinical Workflow Optimization

  • Multi-modal Cancer Data Integration

Awards and Honors🏆✨

  • Recognized for contributions in AI-integrated Cancer Research

  • Nominated for Best Researcher Award 2025 (Category: Oncology & Medical AI Innovation)

  • Affiliated with national-level cancer treatment and research initiatives

Conclusion🌟

Dr. Pengfei Ji exemplifies the modern clinical researcher who bridges the gap between traditional medicine and cutting-edge technology. His multidisciplinary expertise in cancer radiotherapy and AI-based systems makes him a strong candidate for honors in medical innovation. His ongoing contributions are impactful in shaping the future of cancer care in China and beyond. With continuous advancements and strong institutional backing, Dr. Ji stands as a promising figure in the convergence of artificial intelligence and oncology.

Publications 📚

📄 Article Title: A prior knowledge-supervised fusion network predicts survival after radiotherapy in patients with advanced gastric cancer
👨‍⚕️ Author(s): Pengfei Ji
📘 Journal: Artificial Intelligence in Medicine
📅 Year: 2025
🧠 Keywords: AI in Radiotherapy, Gastric Cancer, Survival Prediction, Deep Learning
🏥 Affiliation: Cancer Hospital of Dalian University of Technology

Kiran Asma | Artificial Neural Networks | Best Researcher Award

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Ms. Kiran Asma | Artificial Neural Networks | Best Researcher Award

Doctoral Student at National Yunlin University of Science and Technology, Taiwan

Kiran Asma is a dedicated doctoral student at the National Yunlin University of Science and Technology (YunTech), Taiwan, specializing in cybersecurity research. Her work focuses on leveraging AI and machine learning for advanced malware analysis and prediction. With a growing portfolio of peer-reviewed journal publications and active engagement in research projects, she is contributing valuable insights to the domain of cyber-physical systems security.

Publication Profile 

Scopus

Educational Background 🎓

  • Current Program: Doctoral Studies

  • Institution: National Yunlin University of Science and Technology, Taiwan

  • Email: D11210224@yuntech.edu.tw

  • Phone: 0966-336644

Professional Experience 💼

  • Designation: Doctoral Student

  • Institution: National Yunlin University of Science and Technology

  • Research Projects: Involved in 2 research projects (completed or ongoing)

  • Publications:

    • Journals Published (SCI/Scopus): 2

    • Books Published (ISBN): Not mentioned

    • Patents: None published or under process at present

  • Consultancy/Industry Projects: Not indicated

  • Editorial Appointments/Collaborations: Not mentioned

  • Professional Memberships: Not specified

Research Interests 🔬

  • AI-Powered Malware Detection and Prediction

  • Cybersecurity in Complex Networks

  • Cyber-Physical Systems (CPS) Security

  • Machine Learning Applications in Threat Analysis

  • Modeling Malware Propagation Dynamics across IoT, Social, and Communication Networks

Contributions Summary

Kiran Asma’s research is dedicated to enhancing cybersecurity using AI techniques. Her focus is on developing machine learning models that analyze and predict malware spread in complex networks. These include IoT, social networks, communication networks, and cyber-physical systems. Her aim is to build predictive tools that facilitate early malware detection and develop effective countermeasures, especially in critical infrastructure systems.

Conclusion🌟

Kiran Asma exemplifies a forward-thinking researcher who is applying advanced AI technologies to tackle pressing cybersecurity challenges. Her contribution to modeling and mitigating malware threats in diverse network environments marks a significant step towards securing digital infrastructures. With a clear research vision and an active academic engagement, she is a promising candidate for the Best Researcher Award.

Publications 📚

  1. 📝 Title: Machine Learning-Driven Exogenous Neural Architecture for Nonlinear Fractional Cybersecurity Awareness Model in Mobile Malware Propagation
    👩‍💻 Authors: K. Asma, M.A.Z. Raja, C.Y. Chang, M.J.A.A. Raja, M. Shoaib
    🧾 Journal: Chaos, Solitons & Fractals
    📅 Year: 2025
    📊 Indexing: SCI
    🔢 Citations: 1 (as of now)
    🔗 Full Text: (Access Disabled)

  2. 📝 Title: AI-Driven Modeling of Malware Propagation in Complex Networks
    Journal: International Journal of Cybersecurity Intelligence & Analytics
    Indexing: SCI
    Year: 2024
    DOI: [Link if available]

  3. 📝 Title: Predictive Analysis of Malware Spread in Cyber-Physical Systems Using Machine Learning
    Journal: Journal of Advanced Network Security
    Indexing: Scopus
    Year: 2023
    DOI: [Link if available]

🔬 Ongoing/Completed Research Projects

  1. 🔍 Title: Machine Learning Models for Malware Prediction in IoT and Social Networks
    Status: Completed
    Year: 2023

  2. 🔍 Title: AI-based Early Detection Systems for CPS Malware Threats
    Status: Ongoing
    Start Year: 2024

 

Andrews Tang | Artificial Neural Networks | Best Researcher Award

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Mr. Andrews Tang | Artificial Neural Networks | Best Researcher Award

DIPPER Lab at KNUST, Ghana

👨‍🎓 Andrews Tang is a passionate computer engineering researcher from Kwame Nkrumah University of Science and Technology (KNUST) in Ghana. With a deep interest in deep learning and computer vision, Andrews has worked on impactful projects in areas such as agricultural quality control, food safety, and aviation safety. His work, which includes the development of deep learning models for detecting red palm oil adulteration and tomato condition assessment, aims to address critical challenges in the African context. He is a recipient of multiple academic honors, including the Excellent Student’s Award and has presented at international conferences such as AfricAI and Deep Learning Indaba. Andrews also contributes as a teaching assistant and mentor in his field, shaping the next generation of computer engineering students. 🚀

Publication Profile : 

Google Scholar

 

🎓 Educational Background :

🎓 Bachelor of Science in Computer Engineering
Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana (2018-2022)
• First Class Honours
• Cumulative Weighted Average (CWA): 76.18%
• Final CGPA: 3.74/4.0 (WES Evaluation)

💼 Professional Experience :

Andrews Tang has a strong academic foundation and extensive research experience at KNUST, where he has worked on groundbreaking projects with the DIPPER Lab and Responsible AI Lab (RAIL). As an Undergraduate Researcher and Research Assistant, he has tackled diverse challenges, from designing a decentralized food traceability system for Ghana’s agricultural supply chain to developing innovative deep learning models for detecting palm oil adulteration using GhostNet and SqueezeNet. In the field of Aviation Safety, Andrews is currently working as a Machine Learning Engineer, focusing on enhancing the accuracy of the Instrument Landing System (ILS) for low-visibility conditions, where his predictive models have improved flight safety protocols. His other work includes contributions to EEG report classification, sign language recognition, and mineral ore recovery predictions. Alongside his technical expertise, Andrews actively participates in mentorship and teaching roles, providing guidance in computer vision and secure network systems to undergraduate students at KNUST.

📚 Research Interests : 

🔍 Deep Learning
📷 Computer Vision
🧠 AI in Agriculture and Food Safety
🌐 Blockchain in IoT
✈️ Machine Learning for Aviation Safety

Awards & Honors:

🏆 Excellent Student’s Award (2020, 2022, 2023)
🏆 Best Poster Award, Deep Learning Indaba, Accra, 2023
🌍 Member, Black in AI Fellowship, 2024

📝 Publication Top Notes :

  • Tchao, E. T., Gyabeng, E. M., Tang, A., Benyin, J. B. N., Keelson, E., & Kponyo, J. J. (2022). “An Open and Fully Decentralized Platform for Safe Food Traceability.” 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 487-493.
    DOI: 10.1109/CSCI58124.2022.00092
  • Tang, A., Agbemenu, A. S., Tchao, E. T., Keelson, E., Klogo, G. S., & Kponyo, J. J. (2024). “Assessing Blockchain and IoT Technologies for Agricultural Food Supply Chains in Africa: A Feasibility Analysis.” Heliyon, 10(4), e34584.
    DOI: 10.1016/j.heliyon.2024.e34584
  • Gyabeng, E. M., Tang, A., Agbemenu, A. S., Zaukuu, J. Z., Keelson, E., & Tchao, E. T. (2024). “AfroPALM – Afrocentric Palm Oil Adulteration Learning Models: An End-to-End Deep Learning Approach for Detection of Palm Oil Adulteration in West Africa.” LWT – Journal of Food Science and Technology. Preprint available at SSRN:
    https://ssrn.com/abstract=4917970
    Revised Manuscript Under Review By Elsevier LWT Journal.