Yousry AbdulAzeem | Artificial Intelligence | Best Researcher Award

Assoc. Prof. Dr. Yousry AbdulAzeem | Artificial Intelligence | Best Researcher Award

Associate Professor at School of Computational Sciences and Artificial Intelligence (CSAI), Zewail City of Science and Technology, Egypt

Assoc. Prof. Dr. Yousry AbdulAzeem is a computer science researcher and educator specializing in distributed database systems, artificial intelligence, and software performance engineering. With over two decades of academic and industry experience, he has contributed to database optimization, software modeling, and AI-driven data analysis. He is currently affiliated with Misr Higher Institute for Engineering and Technology, University of Science and Technology (Zewail City, Giza, Egypt), and The Higher Institute of Engineering, El-Shorouk Academy, Cairo, Egypt.

Publication Profile

Scopus

Google Scholar

Educational Details

  • Ph.D. in Automatic Control Engineering (2014) – Mansoura University, Egypt
    Thesis: Ranking of Distributed Uncertain Database Systems
  • M.Sc. in Automatic Control Engineering (2009) – Mansoura University, Egypt
    Thesis: Performance Evaluation of UML Software Models Based on LQN
  • B.Sc. in Computers and Systems Engineering (2004) – Mansoura University, Egypt (Very Good)

Professional Experience

Dr. Yousry AbdulAzeem is an Associate Professor of Computer Science and Engineering with extensive academic and industry experience. He is currently a full-time Associate Professor at the Misr Higher Institute for Engineering and Technology, Mansoura, Egypt, and also serves as an Adjunct Associate Professor at both the University of Science and Technology (Zewail City, Giza) and The Higher Institute of Engineering (El-Shorouk Academy, Cairo, Egypt).

From 2015 to 2022, he worked as an Assistant Professor at Taibah University, Saudi Arabia, where he taught courses in network security, programming, database systems, and artificial intelligence. Before this, he held academic roles at Mansoura University and Misr Higher Institute for Computers and Commerce.

In addition to his academic career, Dr. AbdulAzeem has industry experience as a Technical Director and Executive Manager at Trust IT (2007–2009) and a Senior Programmer & Project Administrator at Aflak Masr for Information Technology (2004–2006). He also has significant experience in academic mentoring and quality assurance, having served as the Academic Mentoring Manager at both Taibah University (2017–2022) and Misr Higher Institute for Engineering and Technology (2023–present).

Research Interest

  • Distributed Database Systems
  • Software Performance Engineering
  • Artificial Intelligence & Knowledge Representation
  • Data Governance & Security
  • Cloud Computing & Distributed Systems

Top Noted Publication

“A CNN-based framework for classification of Alzheimer’s disease” – Neural Computing and Applications (2021)

  • Citations: 127
  • Developed a deep learning framework for Alzheimer’s disease classification using CNNs.

“A congestion-aware clustering and routing (CCR) protocol for mitigating congestion in WSN” – IEEE Access (2019)

  • Citations: 61
  • Proposed a congestion-aware routing protocol for wireless sensor networks.

“Human action recognition based on transfer learning approach” – IEEE Access (2021)

  • Citations: 53
  • Applied deep transfer learning for human activity recognition in videos.

“An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest X-ray images” – PeerJ Computer Science (2021)

  • Citations: 39
  • Introduced an optimized AI model for COVID-19 detection using X-ray images.

“Classification of breast cancer using a manta-ray foraging optimized transfer learning framework” – PeerJ Computer Science (2022)

  • Citations: 27
  • Developed a bio-inspired optimization technique for breast cancer classification.

Conclusion

Assoc. Prof. Dr. Yousry AbdulAzeem is a highly qualified and impactful researcher in Artificial Intelligence and Distributed Systems. His strong publication record, interdisciplinary expertise, and leadership roles make him a top contender for the Best Researcher Award.

To further strengthen his case, he could increase grant acquisitions, expand international collaborations, and focus on AI deployment in real-world applications. Given his high citation impact, research relevance, and academic leadership, he is a strong candidate deserving recognition.

 

 

 

 

Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

Ms. Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

Software Engineer at Skyline Software Solutions, India

Ms. Swati Patel is an accomplished Data Analyst and Software Developer with extensive expertise in data science, machine learning, and software engineering. She has contributed significantly to academic research, publishing 8 journal papers, 2 IEEE conference papers, and 5 books on topics ranging from software security to predictive analytics. With strong analytical skills and a track record of developing efficient workflows and impactful applications, she has consistently delivered data-driven solutions for business optimization and research innovation.

Publication Profile

Google Scholar

Educational Details

Ms. Swati Patel holds a Master of Science in Advanced Computing Technologies from Birkbeck, University of London (2022-2023), where her projects included Principal Component Analysis on the Pima Indians Diabetes Dataset and predicting DDoS attacks using Darknet Time-Series data. She earned a Master’s degree in Computer Science from SES’s R. C. Patel Institute of Technology (NMU, India, 2012-2014), completing her thesis on software birthmark-based theft detection of JavaScript programs. Her Bachelor’s degree in Computer Engineering was completed at PSGVPM’s D. N. Patel College of Engineering, Shahada (NMU, India, 2008-2012), where she developed an Online Voting System using ASP.NET and SQL.

Professional Experience

Swati Patel is a Data Analyst and Entrepreneur with proven expertise in designing and optimizing workflows, data visualization, and software development. She served as a Data Analyst at SSP Group PLC, UK (2023-2024), where she created interactive Power BI dashboards to analyze sales data, optimized EPOS systems for data accuracy, and reduced data processing time by 30% through SQL optimization. As an entrepreneur, she successfully led Skyline Software Solutions (2014-2022), developing over 35 applications in Java, .NET, and other technologies. She managed end-to-end project execution, providing high-quality software solutions to diverse industries.

Research Interest

Swati’s research focuses on applied machine learning, natural language processing, and predictive modeling. She is particularly interested in statistical methods for data anomaly detection, speech-based health diagnostics, and secure systems in computing environments. Her work also extends to innovative clustering techniques for theft detection in software and dimensionality reduction in large datasets.

Author Metrics

  • Publications: 2 IEEE papers, 8 journal papers, 5 books.
  • Key Topics: Data science, machine learning, NLP, secure computing systems, predictive analytics.
  • Notable Works:
    • “A Review on Statistical Analysis-Based Approaches for Data Poison Detection Using Machine Learning.”
    • “Automated Depression Assessment from Speech Signals Using Pitch and Energy Features.”
    • “Long Short-Term Memory and Gated Recurrent Unit Networks for Accurate Stock Price Prediction.”
    • Books: COVID-19 Data Analysis for the United Kingdom and Data Exploration and Machine Learning using R.

Publication Top Notes

  • Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Frequent Subgraph Mining
    • Authors: S.J. Patel, T.M. Pattewar
    • Conference: 2014 International Conference on Embedded Systems (ICES)
    • Pages: 63–68
    • Citations: 9
    • Year: 2014
    • Summary: This paper presents a novel method for detecting software theft in JavaScript programs using software birthmarks. The approach employs agglomerative clustering and frequent subgraph mining to identify similarities between programs, aiding in theft detection.
  • K-Means Clustering Algorithm: Implementation and Critical Analysis
    • Author: S. Patel
    • Publisher: Scholars’ Press
    • Citations: 8
    • Year: 2019
    • Summary: This publication provides an in-depth exploration of the K-means clustering algorithm, including its implementation and a critical analysis of its efficiency and limitations in clustering diverse datasets.
  • Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Improved Frequent Subgraph Mining
    • Authors: S. Patel, T. Pattewar
    • Conference: 2014 International Conference on Advances in Electronics, Computers, and Communications
    • Citations: 7
    • Year: 2014
    • Summary: This paper builds upon earlier research by introducing an improved method for frequent subgraph mining, enhancing the detection accuracy of JavaScript program theft through advanced birthmark-based techniques.
  • Software Birthmark for Theft Detection of JavaScript Programs: A Survey
    • Authors: S.J. Patel, T.M. Pattewar
    • Journal: IJAFRC (International Journal of Advanced Foundation and Research in Computers)
    • Volume: 1, Issue 2, Pages: 29–38
    • Citations: 2
    • Year: 2014
    • Summary: This survey reviews existing methods for detecting software theft in JavaScript programs, with a focus on software birthmark techniques. It outlines challenges, solutions, and future research directions.
  • Emerging Trends in Computer Technology (NCETCT)
    • Authors: S.J. Patel, T.M. Pattewar
    • Journal: IJCA (International Journal of Computer Applications)
    • Conference Issue: NCETCT, Number 1
    • Year: 2014
    • Summary: This conference paper discusses advancements in computer technology with a focus on software security. It explores the use of software birthmarks as a means of detecting and preventing intellectual property theft in programming.

Conclusion

Ms. Swati Patel is a highly qualified and deserving candidate for the Best Researcher Award. Her combination of academic excellence, impactful research, and real-world contributions to artificial intelligence and software engineering set her apart as an innovative thinker. To maximize her potential and visibility, she could focus on strengthening her citation impact, pursuing international collaborations, and contributing to higher-impact conferences. Overall, her track record reflects dedication, skill, and the ability to drive meaningful advancements in her field.