Dr. Zakria Qadir: Leading Researcher in Computer Engineering
š Congratulations Dr. Zakria QadirĀ on Winning the Most Reader’s Article Award! š Your dedication to research, mentorship, and collaboration with international teams is truly commendable. This award is a testament to your outstanding work and the impact it has on the broader community.
Professional Profile:
š¬ Research Focus: Enthusiastic PostDoc Research Associate at UNSW Artificial Intelligence Institute, dedicated to pushing boundaries in DIGITECH. Research spans Artificial Intelligence, Machine Learning, Wireless Communication, IoT, and Cybersecurity. Highly cited Young STEM Researcher.
š Education:
- Ph.D. in Electrical and Computer Engineering (Western Sydney University).
- Research: Smart UAVs for disaster relief, AI, ML, IoT applications.
- Master’s in Sustainable Environment and Energy System (METU).
- Thesis: Neural Network-Based Prediction Algorithms for Hybrid PV-Wind System.
- Bachelor of Science in Electronic Engineering (UET Taxila).
- Gold Medal Award for securing First Position.
š Achievements:
- Google Scholar: Citations 1000+, H-Index 17, Total Papers 40, Cumulative Impact Factor >100+.
- Keynote Speaker at Core A conferences.
- Fully Funded ARC Research Discovery Scholarship for Ph.D.
- Various scholarships and awards for academic excellence.
šØāš» Professional Experience:
- Post-Doc Research Associate at UNSW, collaborating with the Department of Defence Australia.
- Research: Drones-aided AI Algorithms for Battlefield Scenarios.
- Research Assistant at UNSW, collaborating with Cisco, focusing on Intelligent Transportation Systems.
- Sessional Lecturer at Victoria University, teaching Data Science, AI, Computer Science, Business Analysis, Networking.
- Casual Lecturer at Western Sydney University (WSU) and Melbourne Institute of Technology (MIT).
- Lecturer at National University of Technology, teaching IoT, AI, and Machine Learning.
- Senior Research Scientist at Imam Abdulrahman Bin Faisal University.
- Graduate Teaching Assistant at Middle East Technical University (METU).
- Lab Engineer at National University of Science and Technology (NUST).
Publication Top Noted:
- Towards 6G Internet of Things: Recent advances, use cases, and open challenges
- A Hybrid Deep Learning Approach for Bottleneck Detection in IoT
- A strong construction of S-box using Mandelbrot set an image encryption scheme
- Resource optimization in UAV-assisted wireless networksāA comprehensive survey
- Autonomous UAV Path-Planning Optimization Using Metaheuristic Approach for Predisaster Assessment
š Skills:
- Programming Languages: MATLAB, Python, C++.
- Metaheuristic Algorithms: PSO, ACO, DGBCO, GWO.
- Machine Learning (AI): Deep learning, Feature Extraction, CNN, FRNN, YOLO.
- Understanding of Arduino, Raspberry Pi, Proteus, Lucid Chart, VOSViewer, LaTeX.
š Teaching Experience:
- Lectured and supervised students at various universities.
- Lesson planning, preparation, and research in diverse areas.
š
Honors and Awards:
- Graduate Teaching Assistant Scholarship at METU.
- Gold Medal Award for securing First Position in Bachelors.
- Best Engineering Project Award at UET Taxila.
š Funding and Recognition:
- Awarded ARC Research Discovery Scholarship, Research Candidate Support Funding, Teaching Assistant Scholarships, Travel Grants, and Research Grants.
- Recognition from Core A conferences and Australia’s Natural Hazard Research.
The paper “A Prototype of an Energy-Efficient MAGLEV Train: A Step Towards Cleaner Train Transport” focuses on the development and evaluation of a prototype Magnetic Levitation (MAGLEV) train with an emphasis on energy efficiency. Below are some key points and important content from the paper:
Abstract:
- Focus: Development and assessment of an energy-efficient MAGLEV train prototype.
- Goal: Contributing to cleaner and more sustainable train transportation.
Introduction:
- Motivation: Addressing the need for environmentally friendly and energy-efficient transportation solutions.
- Importance of MAGLEV: Highlighting the advantages of MAGLEV technology, such as reduced friction and energy consumption.
Key Features of the MAGLEV Prototype:
- Energy Efficiency Measures: Description of features and technologies incorporated to enhance energy efficiency.
- Magnetic Levitation System: Explanation of the MAGLEV technology used in the prototype.
- Propulsion System: Details about the propulsion mechanism and its role in energy savings.
Performance Evaluation:
- Energy Consumption Analysis: Quantitative assessment of energy consumption compared to traditional train systems.
- Environmental Impact: Discussion on the potential reduction in carbon footprint and environmental benefits.
Results and Findings:
- Energy Savings Percentage: Presentation of the achieved energy savings compared to conventional trains.
- Operational Stability: Evaluation of the MAGLEV prototype’s stability during operations.
Conclusion:
- Significance: Emphasizes the significance of developing energy-efficient transportation solutions.
- Future Implications: Discusses the potential widespread adoption of MAGLEV technology for cleaner and sustainable train transport.
Impact and Citations:
- Citation Count: Indicates the paper’s impact and recognition within the research community.
- Reader’s Count: Reflects the broader readership and interest in the paper’s findings.
Innovation and Contribution:
- Novelty: Highlights any novel approaches, technologies, or methodologies introduced in the MAGLEV prototype.
- Contribution to the Field: Describes how the research contributes to advancements in cleaner and energy-efficient transportation.
This summary provides a glimpse into the essential content of the paper, focusing on its goals, methodology, findings, and impact on the field of transportation and energy efficiency.