Kyeong Kang | Computer Science and Artificial Intelligence | Innovative Research Award

Published on by Research Awards

Innovative Research Award

Kyeong Kang
University of Technology Sydney, Australia

Kyeong Kang
AffiliationUniversity of Technology Sydney
CountryAustralia
Google Scholar ID5-h0TvcAAAAJ
Documents116
Citations1770
h-index24
Subject AreaComputer Science and Artificial Intelligence
EventInternational Research Awards
ORCID0000-0003-4252-9802

The Innovative Research Award recognizes sustained scholarly achievement and research innovation demonstrated through scientific publications, academic influence, and contributions to the advancement of knowledge. Kyeong Kang of the University of Technology Sydney has established a research profile in Computer Science and Artificial Intelligence through peer-reviewed publications, scholarly collaboration, and measurable citation impact.[1] The recognition aligns with the objectives of the International Research Awards, which acknowledge researchers whose work supports innovation, academic excellence, and interdisciplinary development.[4]

Abstract

Kyeong Kang has developed an academic record characterized by peer-reviewed research, interdisciplinary collaboration, and contributions to Computer Science and Artificial Intelligence. Publication output, citation performance, and scholarly visibility indicate sustained engagement with contemporary research topics and international academic communication.[1][2]

Keywords

Artificial Intelligence, Computer Science, Machine Learning, Intelligent Systems, Data Analytics, Academic Research

Introduction

Research in Artificial Intelligence and Computer Science continues to influence scientific discovery, industrial innovation, and digital transformation. Academic contributions within these disciplines are evaluated using publication quality, citation impact, collaboration networks, and research relevance. Kyeong Kang’s scholarly record reflects active participation in these areas through internationally disseminated research outputs.[1]

Research Profile

The research profile includes 116 indexed scholarly documents, approximately 1,770 citations, and an h-index of 24. These bibliometric indicators demonstrate consistent publication activity and measurable academic influence across Computer Science and Artificial Intelligence research domains.[1]

Research Contributions

The research portfolio encompasses investigations in intelligent computing, artificial intelligence methodologies, computational modelling, and advanced software systems. Contributions have supported the development of scalable computational approaches, improved analytical methodologies, and interdisciplinary applications that connect theoretical computer science with practical technological solutions.[2][3]

Publications

Published work has appeared through peer-reviewed scholarly venues and has contributed to ongoing developments within Artificial Intelligence and Computer Science. Research dissemination through indexed journals and conference proceedings has increased scholarly visibility while supporting knowledge exchange across the international research community.[1][2]

Research Impact

Bibliometric indicators provide evidence of scholarly influence through citation activity, publication productivity, and sustained engagement with the research community. Such metrics are commonly used alongside qualitative assessment when evaluating academic achievement and research excellence.[1]

Award Suitability

Based on documented scholarly productivity, citation performance, institutional affiliation, and continued contributions to Computer Science and Artificial Intelligence, Kyeong Kang demonstrates characteristics consistent with the objectives of the Innovative Research Award. Recognition acknowledges measurable academic achievement, research dissemination, and sustained commitment to scientific advancement.[4]

Conclusion

Kyeong Kang’s academic profile illustrates sustained research productivity, recognized scholarly impact, and continued participation in the advancement of Computer Science and Artificial Intelligence. The available bibliometric indicators and institutional research activities support consideration for academic recognition within the International Research Awards framework.[1][4]

References

  1. Google Scholar. (2026). Scholar profile: Kyeong Kang.
    https://scholar.google.com/citations?hl=en&user=5-h0TvcAAAAJ
  2. ORCID. (2026). Kyeong Kang ORCID Record.
    https://orcid.org/0000-0003-4252-9802
  3. Kyeong Kang (2026). Research Output.
    https://profiles.uts.edu.au/Kyeong.Kang/publications
  4. International Research Awards. (2026). International Research Awards Official Website.
    https://researchawards.net/

Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award

Published on by Research Awards

Dr. Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award

Nanyang Technological University at School of Computer Science and Engineering, Nanyang Technological University, Singapore.

Mohamed R. Shoaib is a dedicated Machine Learning and Data Scientist Engineer with extensive expertise in AI and its applications. He holds a Master’s degree in Engineering Science from Menoufia University and is currently pursuing a PhD at Nanyang Technological University. Mohamed has received certifications from Udacity, DataCamp, IBM, and Udemy, demonstrating proficiency in machine learning, deep learning, NLP, and AI. He has a rich professional background, including his role at Shgardi Company where he works on recommendation systems, fraud detection, and user interaction analysis. His research interests span the utilization of AI in biomedical applications, smart agriculture, and global food security. Mohamed’s skills encompass machine learning, deep learning, computer vision, NLP, and embedded systems, making him a valuable asset in the AI and data science community.

Professional Profiles:

Education 🎓

Mohamed R. Shoaib is currently pursuing his Doctor of Philosophy at Nanyang Technological University (NTU), Singapore, within the School of Computer Science and Engineering (SCSE). He began this journey in January 2023, focusing his research on Machine Learning, Data Science, and Artificial Intelligence, particularly their applications in Biomedical, Agriculture, and communication sectors. Prior to this, Mohamed completed his Master’s Degree in Engineering Science from the Faculty of Engineering, Menoufia University, from October 2019 to March 2022. His thesis centered on the Utilization of Artificial Intelligence Techniques in Healthcare Applications, earning a Pre-Master GPA of 3.46/4. Additionally, Mohamed holds a Diploma in Artificial Intelligence from the Information Technology Institute (ITI), which he completed in collaboration with EPITA School of Engineering and Computer Science, between April 2021 and January 2022.

Professional Experience 💼

Mohamed R. Shoaib is currently working as a full-time Machine Learning Engineer and Data Scientist at Shgardi Company, a position he has held since February 2022. In this role, he leverages Amazon Personalized tools and time-series data to enhance recommendation systems. He is also involved in developing solutions to detect fraud and fake user interactions. Alongside his professional role, Mohamed is a full-time researcher at Nanyang Technological University (NTU), Singapore, where he focuses on applying AI in Biomedical, Agriculture, and communication applications. His academic and professional endeavors demonstrate a robust integration of advanced AI techniques to address practical challenges in various fields.

Research Interest 🔍

Mohamed R. Shoaib’s research interests lie at the intersection of machine learning, data science, and artificial intelligence, with a specific focus on their applications in healthcare and environmental fields. He is particularly interested in utilizing artificial intelligence techniques for critical environmental applications, including smart agriculture and satellite imagery analysis. His work also extends to developing deep learning models for biomedical applications, such as brain tumor diagnosis, and enhancing recommendation systems using advanced AI tools. Mohamed is dedicated to advancing the practical applications of AI to solve real-world problems, improve healthcare outcomes, and contribute to sustainable environmental practices.

Awards and Honors 🏆

Mohamed R. Shoaib has received several prestigious awards and honors recognizing his contributions to the fields of machine learning, data science, and artificial intelligence. His outstanding academic and research performance has earned him the Global Health Impact Award 2024 for his innovative work in applying AI to healthcare. He was also recognized by Udacity with a Nanodegree certification in AWS Machine Learning Foundations in 2021, demonstrating his expertise in leveraging cloud-based tools for AI applications. Additionally, Mohamed has been honored by DataCamp and IBM for his proficiency in data science, machine learning, and natural language processing, having completed extensive certification programs in these areas. These accolades reflect his commitment to advancing the field of AI and his exceptional skills in utilizing AI techniques for impactful and transformative solutions.

Research Skills 🧠

Mohamed R. Shoaib is highly skilled in machine learning, deep learning, and AI, specializing in areas such as transfer learning, object detection, and time series forecasting. He excels in computer vision, image processing, NLP, and recommender systems, with additional strengths in reinforcement learning and embedded systems. Proficient in C, Python, and MATLAB, Mohamed is also adept at using AI frameworks like TensorFlow, Keras, and Scikit-learn, as well as Big Data tools like Spark. His problem-solving abilities and expertise in data visualization and dashboard creation with Plotly enhance his capability to develop innovative solutions. Mohamed’s broad skill set equips him to address complex AI challenges and contribute significantly to the field.

Publications

  1. Efficient Framework for Brain Tumor Detection Using Different Deep Learning Techniques
    • Authors: F Taher, M Shoaib, HM Emara, KM Abdelwahab, A El-Samie, E Fathi, …
    • Journal: Frontiers in Public Health
    • Year: 2022
    • Citations: 22
  2. Deep convolutional neural networks for COVID‐19 automatic diagnosis
    • Authors: Emara HM, Shoaib MR, Elwekeil M, El‐Shafai W, Taha TE, …
    • Journal: Microscopy Research and Technique
    • Year: 2021
    • Citations: 22
  3. Hybrid classification structures for automatic COVID-19 detection
    • Authors: MR Shoaib, HM Emara, M Elwekeil, W El-Shafai, TE Taha, AS El-Fishawy, …
    • Journal: Journal of Ambient Intelligence and Humanized Computing
    • Year: 2022
    • Citations: 16
  4. Deepfakes, misinformation, and disinformation in the era of frontier AI, generative AI, and large AI models
    • Authors: MR Shoaib, Z Wang, MT Ahvanooey, J Zhao
    • Conference: 2023 International Conference on Computer and Applications (ICCA)
    • Year: 2023
    • Citations: 13
  5. Simultaneous super-resolution and classification of lung disease scans
    • Authors: HM Emara, MR Shoaib, W El-Shafai, M Elwekeil, EED Hemdan, …
    • Journal: Diagnostics
    • Year: 2023
    • Citations: 13
  6. Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques
    • Authors: MR Shoaib, MR Elshamy, TE Taha, AS El‐Fishawy, FE Abd El‐Samie
    • Journal: Concurrency and Computation: Practice and Experience
    • Year: 2022
    • Citations: 13
  7. Efficient brain tumor detection based on deep learning models
    • Authors: MR Shoaib, MR Elshamy, TE Taha, AS El-Fishawy, FE Abd El-Samie
    • Journal: Journal of Physics: Conference Series
    • Year: 2021
    • Citations: 13
  8. Automatic modulation classification with 2D transforms and convolutional neural network
    • Authors: HS Ghanem, MR Shoaib, S El‐Gazar, H Emara, W El‐Shafai, …
    • Journal: Transactions on Emerging Telecommunications Technologies
    • Year: 2022
    • Citations: 9
  9. Automated diagnosis of EEG abnormalities with different classification techniques
    • Authors: E Abdellatef, HM Emara, MR Shoaib, FE Ibrahim, M Elwekeil, W El-Shafai, …
    • Journal: Medical & Biological Engineering & Computing
    • Year: 2023
    • Citations: 4
  10. A survey on the applications of frontier AI, foundation models, and large language models to intelligent transportation systems
    • Authors: MR Shoaib, HM Emara, J Zhao
    • Conference: 2023 International Conference on Computer and Applications (ICCA)
    • Year: 2023
    • Citations: 4