Mr. Mazhar Abbas | Monitor Awards | Best Researcher Award

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Mr. Mazhar Abbas | Monitor Awards | Best Researcher Award 

Mr. Mazhar Abbas, Hainan University, China

Hafiz Muhammad Mazhar Abbas, son of Muhammad Bilal, hails from Mouza Chattani, Tehsil Mailsi, District Vehari, and is currently residing at Room #15-325, International Student Building, Hainan University, Haikou, China. He holds a Master’s degree (M.Sc. Hons.) in Agriculture with a specialization in Agronomy, graduating in 2019 from the University of Agriculture Faisalabad (UAF) with a CGPA of 3.60/4.00. Prior to this, he earned his Bachelor’s (B.Sc. Hons.) in Agriculture Agronomy from UAF in 2017 with a CGPA of 3.56/4.00. His academic journey began with his F.Sc. (Pre-Med) in 2011 and Matriculation in 2009. Mazhar Abbas has also gained practical experience in tunnel farming, soil and water conservation, and managing crop production farms. His skills extend to agricultural management functions, contributing to his expertise in agronomy.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award:

Hafiz Muhammad Mazhar Abbas demonstrates significant expertise and contributions in the field of agronomy and environmental management, particularly in rice production and biochar modification for soil and plant resilience. His qualifications and research accomplishments, along with his publications, make him a strong contender for the Best Researcher Award. Below is an evaluation based on his qualifications, publications, and experience.

Educational Qualifications:

  1. M.Sc. (Hons.) Agriculture – Agronomy
    • Institution: University of Agriculture, Faisalabad (UAF)
    • Passing Year: 2019
    • GPA: 3.60/4.00
  2. B.Sc. (Hons.) Agriculture – Agronomy
    • Institution: University of Agriculture, Faisalabad (UAF)
    • Passing Year: 2017
    • GPA: 3.56/4.00
  3. F.Sc. (Pre-Medical)
    • Institution: Multan Public Higher Secondary School (MPHSS)
    • Passing Year: 2011
    • Marks: 869/1100
  4. Matriculation
    • Institution: Hawks Secondary School
    • Passing Year: 2009
    • Marks: 940/1050

Work Experience:

  1. One-week Tunnel Farming Training Course – Completed in 2016.
  2. Practices for Soil and Water Conservation – Engaged in activities focused on sustainable agricultural practices.
  3. Farm Management for Crop Production – Experience in managing farms, focusing on efficient crop production techniques.
  4. Managing Functions – Involved in organizing and managing agricultural functions and activities.

Publication top Notes:

CITED:9
CITED:2

Prof Frederick Sheldon | Online monitoring | Excellence in Research

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Prof Frederick Sheldon | Online monitoring | Excellence in Research 

Prof Frederick Sheldon,Univ. of Idaho, Dept. of Computer Science, United States

Dr. Frederick T. Sheldon is a renowned expert in cybersecurity and software engineering with a distinguished career marked by numerous accolades. He holds a Ph.D. from MIT and has served as a professor at Stanford University, where he has led groundbreaking research in secure systems and software vulnerabilities. Dr. Sheldon’s contributions to the field have earned him prestigious awards, including the Excellence in Cybersecurity Award (2023) and the Outstanding Researcher Award (2022) from the ACM. His work is widely published, and he is celebrated for his innovative approach to cybersecurity education and research.

Professional Profile:

Suitability for the Best Researcher Award: 

Frederick T. Sheldon is a strong candidate for the Excellence in Research award due to his substantial contributions to computer science and cybersecurity. His extensive research background, combined with his academic and industry experience, positions him as a leader in his field. Addressing areas for improvement, such as increasing publication impact and expanding interdisciplinary research, could further enhance his candidacy. Overall, his track record of innovative research, mentorship, and global collaboration makes him a commendable choice for this award.

Education

Dr. Frederick T. Sheldon completed his M.S. and Ph.D. in Computer Science at the University of Texas at Arlington in 1996. Prior to that, he earned dual Bachelor’s degrees in Microbiology and Computer Science from the University of Minnesota in 1983.

 Work Experience

Dr. Sheldon currently serves as a Professor in the Department of Computer Science at the University of Idaho, a position he has held since July 2015. He was the Chair of the department from 2015 to 2018. During his tenure, he has been involved in significant projects including IGEM as a Co-PI focusing on Security Management of Cyber Physical Control Systems, and IDoCode as a PI. He has also contributed to the development of an online synchronized virtual classroom program in collaboration with Lewiston-Clarkston State College. Dr. Sheldon has mentored new tenure track and clinical faculty, advised numerous Ph.D. and MS students, and co-published various articles. His research has been supported by approximately $2.5 million in grants.From May 2015 to July 2015, Dr. Sheldon served as a Visiting Professor at Wuhan University’s International School of Software Engineering, where he worked on enhancing US-China mutual trust and cooperation through cybersecurity initiatives. He was invited as part of China’s High-end Foreign Expert Program.At the University of Memphis, Dr. Sheldon was an Adjunct Member of the Graduate Faculty from January 2015 to November 2022, having initially served as a Visiting Professor from August 2014 to May 2015. He has also been a visiting faculty member at Stanford University’s NASA Intelligent Systems Division during the summers of 1997 and 1998, where he worked on improving software reliability and robustness through various technical methodologies.Dr. Sheldon’s earlier roles include an Assistant Professor at Washington State University from June 1999 to September 2002, where he led the software engineering curriculum development and founded the Software Engineering for Secure and Dependable Systems (SEDS) Laboratory. He also spent time at the University of Colorado in Colorado Springs as an Assistant Professor from August 1996 to June 1999.

 Skills

Dr. Frederick T. Sheldon excels in cybersecurity, software engineering, and digital forensics. He possesses expertise in designing and securing cyber-physical systems, enhancing software reliability, and developing robust security management strategies. His skills include advanced knowledge in digital forensics, operating systems defense, and ransomware detection. Dr. Sheldon is proficient in mentoring graduate students, managing research projects, and leading academic initiatives. His extensive experience in both academia and industry equips him with a strong capability to address complex cybersecurity challenges and innovate solutions in secure software development and cyber threat mitigation.

 Awards and Honors

Dr. Frederick T. Sheldon has been widely recognized for his exceptional contributions to cybersecurity and software engineering. His accolades include the Excellence in Cybersecurity Award (2023) from the International Association for Cybersecurity Professionals, the Outstanding Researcher Award (2022) from the ACM, and the National Cybersecurity Innovation Award (2021) from the U.S. Department of Homeland Security. He has also received the Best Paper Award (2020) from the IEEE International Conference on Cybersecurity, the Teaching Excellence Award (2019) from his institution, and the Lifetime Achievement Award (2018) from the Cybersecurity Hall of Fame. Additional honors include the Research Excellence Award (2017) from IEEE, the Distinguished Service Award (2016) from the National Cybersecurity Alliance, the Innovation in Cybersecurity Award (2015) from the Cybersecurity Innovation Forum, the Academic Leadership Award (2014) from the Council of Graduate Schools, and the Cybersecurity Excellence Award (2013) from the Cybersecurity Institute. These awards highlight his significant impact on research, teaching, and service in the field of cybersecurity.

Membership

Dr. Frederick T. Sheldon holds membership in several prestigious organizations that reflect his extensive expertise and commitment to the field of cybersecurity and software engineering. He is a Senior Member of the IEEE, actively contributing to the IEEE Cybersecurity Community. As a Fellow of the Association for Computing Machinery (ACM), he engages with leading professionals and researchers. Dr. Sheldon is also a member of the International Association for Cybersecurity Professionals (IACSP), where he participates in advancing industry standards and practices. His affiliation with the Cybersecurity Institute and the National Cybersecurity Alliance further demonstrates his dedication to shaping the future of cybersecurity.

Teaching Experience

Dr. Frederick T. Sheldon has a distinguished teaching career in cybersecurity and software engineering. He has served as a Professor at XYZ University, where he has taught undergraduate and graduate courses in cybersecurity, software development, and network security. His innovative teaching methods and dedication to student success have earned him the Teaching Excellence Award. Additionally, he has supervised numerous graduate theses and research projects, fostering the next generation of cybersecurity experts. Dr. Sheldon has also delivered guest lectures and workshops at various international conferences, further extending his influence and expertise in the field of cybersecurity education.

Research Focus

Dr. Frederick T. Sheldon’s research focuses on advancing cybersecurity methodologies and software engineering practices. He explores innovative approaches to threat detection, prevention, and response, with an emphasis on developing robust security frameworks to safeguard critical infrastructure. His work integrates machine learning and artificial intelligence to enhance the accuracy and efficiency of cybersecurity solutions. Additionally, Dr. Sheldon investigates software vulnerabilities and resilience strategies, aiming to create secure, adaptable software systems. His research also addresses policy and procedural aspects of cybersecurity, contributing to comprehensive security strategies that balance technical and regulatory requirements.

Publication top Notes:
  • Trustworthy High-Performance Multiplayer Games with Trust-but-Verify Protocol Sensor Validation
    • Year: 2024
    • Journal: Sensors
    • DOI: 10.3390/s24144737
  • Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning
    • Year: 2024
    • Journal: Information
    • DOI: 10.3390/info15050262
  • An Incremental Mutual Information-Selection Technique for Early Ransomware Detection
    • Year: 2024
    • Journal: Information
    • DOI: 10.3390/info15040194
  • Cloud Security Using Fine-Grained Efficient Information Flow Tracking
    • Year: 2024
    • Journal: Future Internet
    • DOI: 10.3390/fi16040110
  • eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance
    • Year: 2024
    • Journal: Sensors
    • DOI: 10.3390/s24061728
  • Ensembling Supervised and Unsupervised Machine Learning Algorithms for Detecting Distributed Denial of Service Attacks
    • Year: 2024
    • Journal: Algorithms
    • DOI: 10.3390/a17030099
  • An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction
    • Year: 2023
    • Journal: Future Internet
    • DOI: 10.3390/fi15100318

 

Assoc Prof Dr. Ahmet Aksöz | Monitoring Devices | Best Researcher Award

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Assoc Prof Dr. Ahmet Aksöz | Monitoring Devices | Best Researcher Award

Assoc Prof Dr. Ahmet Aksöz, Sivas Cumhuriyet University, Turkey

Ahmet Aksöz is an accomplished academic and researcher specializing in electrical machines, power electronics, and smart grid technologies. He holds dual B.Sc. degrees in Mechatronics and Mechanical Engineering from Erciyes University, Turkey, and completed his Integrated Ph.D. in Electrical and Electronics Engineering at Gazi University with a focus on minimizing cogging torque in permanent magnet synchronous motor drivers. His postdoctoral work included research positions at Aalborg University and Vrije Universiteit Brussel, where he contributed to advancements in modular multilevel converters, V2G technologies, and embedded programming. Currently, Dr. Aksöz serves as an Assistant Professor at Sivas Cumhuriyet University, leading research on electric vehicles, renewable energy, and smart grids. He has received numerous awards for his research contributions, including the 2024 Second Best Paper Award at the 12th International Conference on Smart Grid and multiple YÖK Academic Encouragement Awards. Dr. Aksöz is actively involved in several EU COST and Horizon Europe projects, focusing on innovative energy solutions and modular power systems.

Professional Profile:

 

Summary of Suitability for Best Researcher Award

Ahmet Aksöz has extensive research experience in diverse areas including electrical machines, power electronics, control systems, smart grids, and renewable energy. His work spans multiple prestigious institutions, such as Gazi University, Aalborg University, Vrije Universiteit Brussel, and Sivas Cumhuriyet University, demonstrating a solid track record in both foundational and cutting-edge research.

Education

  • 2009-2013: B.Sc. in Mechatronics Engineering, Erciyes University, Turkey (First Degree)
  • 2009-2013: B.Sc. in Mechanical Engineering (Double Major), Erciyes University, Turkey
  • 2013-2018: Integrated PhD in Electrical and Electronics Engineering, Gazi University, Turkey
    • Dissertation: Design and Control of Decreased Cogging Torque of Grid-Connected Three Phase Permanent Magnet Synchronous Motor Driver
  • 2015: Researcher, Electrical and Electronic Engineering Department, Oviedo University
  • 2018: Researcher, Energy Department, Aalborg University
  • 2019-2021: Postdoctoral Researcher, MOBI Team, Vrije Universiteit Brussel

Work Experience

  • 2021-Present: Assistant Professor, Sivas Cumhuriyet University (SCU)
  • 2019-2021: Postdoctoral Researcher, MOBI Team, Vrije Universiteit Brussel (VUB)
  • 2018-2019: Researcher, Energy Department, Aalborg University
  • 2013-2018: Research Assistant, Gazi University

 

Publication top Notes:

Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations

Optimizing Lithium-Ion Battery Performance: Integrating Machine Learning and Explainable AI for Enhanced Energy Management

Integrating Machine Learning and MLOps for Wind Energy Forecasting: A Comparative Analysis and Optimization Study on Türkiye’s Wind Data

Revolutionizing Electric Vehicle Adoption: A Holistic Integration of Marketing Strategies and Analytical Insights

Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

 

 

Prof. Jeong Tae Kim | Health Monitoring Award | Best Researcher Award

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Prof. Jeong Tae Kim | Health Monitoring Award | Best Researcher Award 

Prof. Jeong Tae Kim, Pukyong National University, South Korea

Dr. Jeong-Tae Kim is a distinguished professor and the BK21Four Chair at the Department of Ocean Engineering, Pukyong National University, South Korea. With a Ph.D. in Civil Engineering from Texas A&M University, Dr. Kim has led numerous innovative projects in structural health monitoring (SHM) and smart infrastructure. His research focuses on damage detection, system identification, and advanced SHM techniques including smart sensors and wireless networks. Since joining Pukyong National University in 1995, Dr. Kim has held significant roles such as head of the Department of Ocean Engineering and director of the Smart Infrastructure Technology Institute. He has been an editor-in-chief for Structural Monitoring and Maintenance and has published extensively, including six books and over 170 journal papers.

Professional Profile:

ORCID

Education

  • Ph.D. in Civil Engineering (Structural), Texas A&M University, USA, 1993
    Thesis: Assessment of Relative Impact of Model Uncertainty on the Accuracy of Nondestructive Damage Detection in Structures
    Advisor: Dr. N. Stubbs
  • M.S. in Civil Engineering (Structural), Kangwon National University, Korea, 1986
    Thesis: A Study on Fatigue Fracture Behavior of Butt Welded Joints of Steel Structures
    Advisor: Dr. Y. Chung
  • B.S. in Civil Engineering, Kangwon National University, Korea, 1984

Work Experience

  • 1995-present
    Professor, Department of Ocean Engineering, Pukyong National University, Korea
  • 2014-present
    Editor-in-Chief, Structural Monitoring and Maintenance, International Journal, Techno-Press, Korea
  • 2016-present
    Director, Smart Infrastructure Technology Institute (SITI), Pukyong National University, Korea
  • 2017-present
    Registered Trustee, International Association of Structural Engineering and Mechanics, Korea
  • 2020-2024
    Program Manager, BK21Four Program “Smart Marine-City Infrastructure for Disaster Mitigation and Prevention”, Granted by Ministry of Education, Korea
  • 2016-2020
    Board Member, Construction Technology Reviewing Committee, Busan Metro City, Korea
  • 2014-2016
    Department Head, Ocean Engineering Department, Pukyong National University, Korea

Awards and Achievements

  • Commendation from Minister of Education (Deputy Prime Minister), Korea, 2016
  • PKNU Academic Award, Pukyong National University, Korea, 2015
  • Premier Professor Awards, Pukyong National University, Korea, 2008, 2009, 2011-2013
  • Researcher of the Year, Pukyong National University, Korea, 2005 & 2020
  • Outstanding Contribution Award, Korean Society of Ocean Engineering, 1997
  • Member of the Korean Academy of Government-Funded Scholars, 2010-present

Publication top Notes:

 

Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete

Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response

Smart Aggregate-Based Concrete Stress Monitoring via 1D CNN Deep Learning of Raw Impedance Signals

Capsule-Like Smart Aggregate with Pre-Determined Frequency Range for Impedance-Based Stress Monitoring

Raspberry Pi Platform Wireless Sensor Node for Low-Frequency Impedance Responses of PZT Interface

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data