Mr. Muhammad Riaz | Efficient Networking | Best Researcher Award

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Mr. Muhammad Riaz | Efficient Networking | Best Researcher Award

Mr. Muhammad Riaz, KP TEVTA, Pakistan

Mr. Muhammad Riaz is a dedicated educator and researcher with expertise in electrical engineering, specializing in power systems and sustainable energy solutions. He holds a Master’s in Electrical Engineering from the University of Wah, where his thesis focused on optimizing power flow in renewable energy-integrated systems. With extensive experience in teaching, laboratory management, and fieldwork, Mr. Riaz has a proven track record of mentoring students and advancing practical engineering solutions. Passionate about smart grids, artificial intelligence, and control systems, he is committed to shaping the next generation of engineers while contributing to innovative energy solutions.

Professional Profile

Orcid

Suitability for Best Researcher Award

Mr. Muhammad Riaz’s dedication to advancing sustainable energy solutions and his impactful contributions to electrical engineering make him a strong candidate for the Best Researcher Award. His research on optimal power flow and renewable energy integration addresses critical challenges in modern power systems. His academic achievements, teaching excellence, and commitment to mentoring future engineers highlight his multifaceted contributions to the field. Mr. Riaz’s innovative approach and focus on practical solutions align perfectly with the award’s vision of recognizing transformative research.

Education

Mr. Muhammad Riaz completed his Master’s in Electrical Engineering at Wah Engineering College, University of Wah, in 2021, specializing in Power Engineering. His thesis, “Optimal Power Flow Solution of Modified IEEE 30 Bus System Integrated with Renewable Energy Sources,” highlights his focus on integrating renewable energy into power systems. He earned his Bachelor’s in Electrical Engineering from Bahria University Islamabad in 2018, where he designed a battery-free MPPT solar inverter as his final year project. His academic journey reflects a strong foundation in electrical and electronics engineering, complemented by minors in computer and communication engineering.

Experience

As a Lecturer at the Govt. Technical & Vocational Training Authority KP since 2021, Mr. Riaz has designed comprehensive lesson plans and delivered engaging lectures on motors, generators, PLCs, and solar PV systems. He also mentors students, fostering innovation and critical thinking. Previously, as a Lab Engineer at Wah Engineering College, he developed and implemented laboratory experiments, collaborated with faculty to optimize resources, and maintained equipment for effective teaching. His hands-on experience and dedication to teaching underscore his commitment to advancing electrical engineering education.

Awards and Honors

  • Fully Funded Scholarship: Secured a scholarship for BEE from the ICT R&D Fund, Government of Pakistan.
  • Rector’s Honor List: Recognized with Cum Laude distinction at Bahria University Islamabad.
  • Prime Minister Laptop Scheme: Awarded under the national initiative for academic excellence.
    These accolades reflect Mr. Riaz’s academic excellence and commitment to professional growth.

Research Focus

Mr. Muhammad Riaz’s research focuses on smart grids, optimal power flow, and the application of artificial intelligence in power systems. His work emphasizes sustainable energy solutions, including the integration of renewable energy into existing grids. His interests extend to control systems and optimization algorithms, aiming to enhance energy efficiency and reliability. His research aligns with global efforts to address energy challenges through innovative and intelligent systems.

Publication Top Notes

  • Article: An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer
    • Year: 2021
  • Article: An Optimization-Based Strategy for Solving Optimal Power Flow Problems in a Power System Integrated with Stochastic Solar and Wind Power Energy
    • Year: 2021
  • Conference Paper: An Innovative Model Based on FCRBM for Load Forecasting in the Smart Grid
    • Year: 2020
  • Conference Paper: Day Ahead Electric Load Forecasting by an Intelligent Hybrid Model Based on Deep Learning for Smart Grid
    • Year: 2020

 

 

Prof. Dr. Resul Das | Computer Networks Awards | Best Faculty Award

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Prof. Dr. Resul Das | Computer Networks Awards | Best Faculty Award

Prof. Dr. Resul Das, Firat Universtiy, Turkey

Prof. Dr. Resul Das is a Full Professor in the Department of Software Engineering at Firat University, Turkey, specializing in software engineering, cyber security, data science, and IoT solutions. He earned his Ph.D. in Electrical-Electronics Engineering from Firat University and later pursued postdoctoral research at the University of Alberta, Canada, where he focused on multi-sensor data fusion and IoT security. Prof. Dr. Das has a strong academic background, with research interests spanning agile software development, network management, machine learning, and smart applications. He has received numerous awards, including TÜBiTAK’s Academic Publication Encouragement Award and the European Union’s Leonardo Da Vinci Education Grant. Prof. Dr. Das is also an active member of professional associations, including the Cisco Networking Academy and the Vocational and Technical Education Development Association. ✨🎉

Publication Profile:

Scopus
Orcid
Google Scholar

Suitability for the Award

Prof. Dr. Resul Das’s extensive academic contributions, global research impact, and leadership in advancing education and research make him a highly suitable candidate for the Research for Best Faculty Award. His work in IoT security, graph data science, and fog computing has not only addressed critical technological challenges but also influenced subsequent research and applications. 💡💥

Academic Background

Prof. Dr. Resul Das earned his Ph.D. in Electrical-Electronics Engineering from Firat University, Turkey (2008), with a focus on Knowledge Extraction from Web User Access Logs. He also completed his M.Sc. in Electronics and Computer Science (2002) from the same institution, specializing in Video Conference System Design. 🔐💡

Research Interests

His expertise spans Software Engineering, Computer Networks, Cyber Security, Data Science, and IoT/M2M Solutions, particularly in multi-sensor data fusion, IoT security, smart grids, fog computing, and big data. His work on cyber-attacks, machine learning, and network optimization contributes to advancing modern tech infrastructure. 🎉💡

Professional Career

Currently a Full Professor at Firat University, Dr. Das has held notable positions as a Visiting Professor at the University of Alberta, Canada, and has led numerous successful research projects in IoT security and data fusion. His contributions have earned him several prestigious awards, including TÜBiTAK Academic Publication Encouragement Award (16 times) and the EU Leonardo Da Vinci Education Grant (2006). 🌍✨

Global Recognition

A TÜBiTAK International Postdoctoral Research Fellowship recipient, Prof. Dr. Das has also contributed significantly as a Cisco Networking Academy Instructor and Project Coordinator. His work has been recognized globally for its impact on cyber security and data science advancements. 🔐💡🌍

Publication Top Notes:

  • Effective diagnosis of heart disease through neural networks ensembles
    • Citations: 848
    • Year: 2009
  • Cyber-security in smart grids: Threats and potential solutions
    • Citations: 729
    • Year: 2020
  • A comparison of multiple classification methods for diagnosis of Parkinson’s disease
    • Citations: 486
    • Year: 2010
  • Performance analysis of classification algorithms on early detection of liver disease
    • Citations: 221
    • Year: 2017
  • A novel honeypot-based security approach for real-time intrusion detection and prevention systems
    • Citations: 139
    • Year: 2018

 

 

Prof. Reza Ghaderi | AI in Networking | Best Faculty Award

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Prof. Reza Ghaderi | AI in Networking | Best Faculty Award

Prof.  Reza Ghaderil, Shahid Beheshti University, Iran

Reza Ghaderi’s extensive achievements in education, research, and academic leadership make him an exemplary candidate for the Research for Best Researcher Award. His educational background, specialized research expertise, and significant academic contributions reflect a career dedicated to advancing electrical engineering and technology. His innovative research projects further illustrate his ability to address complex challenges and drive progress in his field. With a proven track record of impactful work and leadership, Dr. Ghaderi embodies the qualities sought for this prestigious award.

Professional Profile:

Scopus 
Orcid
Google scholar

Suitability for the Award

Prof. Reza Ghaderi is highly suitable for the Research for Best Faculty Award. His extensive academic and research experience, combined with his leadership roles in both educational institutions and national projects, make him a distinguished figure in the field of electrical engineering. His contributions to research, particularly in neural networks, nanoelectronics, and particle accelerators, have had a significant impact on both the academic community and the broader technological landscape.

Educational Achievements:

Reza Ghaderi’s educational background is marked by significant accomplishments, reflecting his deep knowledge and commitment to the field of electrical engineering. He earned his Bachelor’s and Master’s degrees in Electrical Engineering – Electronics from Ferdowsi University of Mashhad and Tarbiat Modares University, respectively. His academic journey culminated in a Ph.D. from the University of Surrey, Guildford, where he specialized in electrical engineering. His strong educational foundation has provided him with the skills and insights necessary for groundbreaking research and leadership in his field.

Specialized Research Expertise:

Dr. Ghaderi’s research expertise spans a wide array of topics within electrical engineering, showcasing his versatility and depth of knowledge. His work includes the design and development of advanced technological systems such as high-voltage generators and digital autopilot systems. He has made significant contributions to neural networks, face recognition systems, and hydraulic servo systems, highlighting his ability to tackle complex problems and innovate solutions in various research areas.

Significant Academic Contributions:

Reza Ghaderi has made notable academic contributions through his roles as a faculty member and administrator at prestigious institutions. His leadership positions at the University of Mazandaran and Shahid Beheshti University, including his current role as Dean of the Faculty of Electrical Engineering, underscore his influence in shaping academic programs and research initiatives. His involvement in the development of national technology and IT strategies further emphasizes his impact on advancing educational and technological standards.

Innovative Research Projects:

Dr. Ghaderi’s innovative research projects have addressed a range of technological and scientific challenges. His work on designing high-voltage generators and AC motor speed controllers, along with his research on neural networks and particle accelerators, demonstrates his ability to lead and execute complex projects. His contributions to national projects, such as the development of sonar strategies and energy strategy documents, highlight his commitment to advancing technology and its applications on a broader scale.

Professional Experience and Impact:

Reza Ghaderi’s professional experience encompasses a broad range of roles and responsibilities that highlight his profound impact on the field of electrical engineering and academia. His career trajectory demonstrates a commitment to both advancing technological innovations and shaping educational practices.

Publication Top Notes:

  • Publication Topic: “A Practical Approach to Tracking Estimation Using Object Trajectory Linearization”
    • Year: 2024
  • Publication Topic: “Rapid and Accurate Predictions of Perfect and Defective Material Properties in Atomistic Simulation Using the Power of 3D CNN-Based Trained Artificial Neural Networks”
    • Year: 2024
  • Publication Topic: “Solving a Class of Thomas–Fermi Equations: A New Solution Concept Based on Physics-Informed Machine Learning”
    • Year: 2024
  • Publication Topic: “Salinity and Flow Pattern Independent Flow Rate Measurement in a Gas-Liquid Flow with Optimum Feature Selection and Novel Detection Geometry Using ANNs”
    • Year: 2024
  • Publication Topic: “An IoT-Based Packet Aggregation Mechanism for the SDN-Based Wide Area Networks”
    • Year: 2024