Prof Frederick Sheldon | Online monitoring | Excellence in Research

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Prof Frederick Sheldon | Online monitoring | Excellence in ResearchĀ 

Prof Frederick Sheldon,Univ. of Idaho, Dept. of Computer Science,Ā 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. Sheldonā€™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:Ā 

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 Bachelorā€™s degrees in Microbiology and Computer Science from the University of Minnesota in 1983.

Ā 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 Universityā€™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 Chinaā€™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. Sheldonā€™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.

Ā 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.

Ā 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. Sheldonā€™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

 

Prof. Irina Shoshina | Accolades Award | Top Researcher Award

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Prof. Irina Shoshina | Accolades Award | Top Researcher AwardĀ 

Prof. Irina Shoshina, Saint-Petersburg state University, Russia

Irina I. Shoshina, Ph.D., is a distinguished Doctor of Biological Sciences and Professor at the Institute for Cognitive Research at St. Petersburg State University. She completed her dissertation at the Pavlov Institute of Physiology PAS in 2015, focusing on “The Global and Local Mechanisms for the Analysis of Visual Information in Normal Subjects and in Schizophrenia.” Her research spans visual perception physiology, changes in perception under extreme conditions, and psychopathology. She has led and participated in various biomedical experiments, including analog studies of “dry” immersion with and without stimulation. Dr. Shoshina has authored over 85 publications, with recent works addressing topics such as visual contrast sensitivity, ocular microtremor, and cognitive functioning in schizophrenia and bipolar disorder. Her significant contributions include studies on eye movements and fatigue detection, as well as the impact of medication on cognitive effects. For more information.

Professional Profile:

Summary of Suitability for Top Researcher Award

Her research covers significant areas in visual perception, extreme environments, and psychopathology. These fields are crucial for advancing our understanding of cognitive and sensory processes.She has authored over 85 publications, with many appearing in high-impact journals. Recent articles include work on visual contrast sensitivity, cognitive functioning in schizophrenia, and fatigue detection based on eye movements. Her work is widely recognized, evidenced by numerous publications in Q1 and Q2 journals.

Education:

  • Doctor of Biological Sciences: Shoshina holds a Ph.D. from the Pavlov Institute of Physiology, PAS. She completed her dissertation in 2015 with the topic: “The Global and Local Mechanism for the Analyses of Visual Information in Normal Subjects and in Schizophrenia.” This research centered around visual perception physiology, particularly focusing on changes in visual perception under extreme conditions and in psychopathological states such as schizophrenia.

Work Experience:

  • Professor, Institute for Cognitive Research: Shoshina is a professor at St. Petersburg State University, where she is actively engaged in research related to cognitive science, visual perception, and physiological responses in extreme environments.
  • Principal Investigator: Shoshina has led numerous studies, including analog experiments simulating extreme conditions like “dry” immersion, with or without different stimulations. Her research frequently explores visual perception changes and cognitive functioning, especially in the context of altered gravity, fatigue, schizophrenia, and bipolar disorder.
  • Co-Investigator: She has also worked as a co-investigator in various biomedical experiments, extending her expertise into interdisciplinary collaborations.
  • Publications and Research: Shoshina has authored more than 85 scientific publications, focusing on visual contrast sensitivity, ocular microtremors, cognitive functions in psychopathological conditions, and the effects of environmental extremes on perception and cognition.

Publication top Notes:

Characteristics of Visual Contrast Sensitivity and Ocular Microtremor in Schizophrenia

Brain atrophy and cognitive decline in bipolar disorder: Influence of medication use, symptomatology and illness duration

OperatorEYEVP: Operator Dataset for Fatigue Detection Based on Eye Movements, Heart Rate Data, and Video Information

Cognitive Functioning and Visual System Characteristics in Schizophrenia: A Cross-Sectional Study

Combined influence of medication and symptom severity on visual processing in bipolar disorder

 

 

Mr. Tamoor Shafique | Devices Award | Best Researcher Award

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Mr. Tamoor Shafique | Devices Award | Best Researcher Award

Mr. Tamoor Shafique, Staffordshire University, United Kingdom

Dr. Tamoor Shafique is a Senior Lecturer in Automation & Robotics Engineering at Staffordshire University, where he also serves as Course Leader for MEng/BEng (Hons) Electrical and Electronic Engineering. With a Ph.D. in Electrical Engineering, a Master’s from CIIT Islamabad, and a Bachelor’s from UCET Mirpur, Dr. Shafique has extensive experience in both academia and industry. He specializes in curriculum development, strategic decision-making, and stakeholder liaison, with a strong track record in quality assurance and educational leadership. Dr. Shafique has published six impactful journal papers and contributed to IEEE conference proceedings. His professional experience includes roles as Deputy Head of Engineering HE at University Centre Wigan & Leigh College and Lecturer at various institutions, including Mirpur University of Science and Technology and Conceptz IT Solutions and Training Institute. He is a Fellow of the Higher Education Academy (FHEA) and holds a PGCert in Education. In addition to his academic roles, Dr. Shafique has been involved in local community service as a Foundation Governor at Great Ashton Academy Trust. His research and teaching focus on Robotics, Electronics, and Automation, and he is committed to enhancing educational standards and student engagement.

Professional Profile:

ORCID

 

Summary of Suitability for Best Researcher Award:

Engr. Tamoor Shafique is a distinguished Senior Lecturer in Automation and Robotics Engineering at Staffordshire University with a comprehensive background in electrical engineering and education. His experience spans both academia and industry, demonstrating a deep commitment to engineering education and research.

Education:

  • PhD: Completion in June 2024. šŸŽ“
  • MSc (Electrical Engineering): CIIT Islamabad, Pakistan (2011-2013). šŸ“Š
  • BSc (Electrical Engineering): UCET Mirpur, Pakistan (2006-2010). šŸ“
  • PGCert in Education (Education and Training): University of Central Lancashire, UK (2022). šŸŒŸ
  • FHEA: Fellowship of Higher Education Academy (2022). šŸŽ“

Professional Experience:

  • Course Leader and Senior Lecturer, Staffordshire University (Jan 2022 ā€“ Present): Overseeing MEng/BEng Electrical and Electronic Engineering, contributing to course re-accreditation, and developing blended learning strategies. šŸ«
  • Deputy Head of Engineering HE, University Centre Wigan & Leigh College (Jun 2017 ā€“ 2022): Managed quality assurance, internal audits, and curriculum development. šŸ”§
  • Controls Engineer, Air Handlers Northern Limited (Oct 2016 ā€“ Feb 2017): Assisted in developing strategies for AHU Controllers. šŸŒ¬ļø
  • Lecturer, Mirpur University of Science and Technology (Sep 2014 ā€“ Sep 2016): Taught and managed various academic responsibilities. šŸ“˜

Interests & Hobbies:

  • Engaging with local communities and volunteering. šŸŒ
  • Playing badminton and reading. šŸøšŸ“š
  • Spending time with family and friends. šŸ‘Øā€šŸ‘©ā€šŸ‘§ā€šŸ‘¦

Publication top Notes:

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs)

A Review of Energy Hole Mitigating Techniques in Multi-Hop Many to One Communication and its Significance in IoT Oriented Smart City Infrastructure

Data Augmentation-Assisted Makeup-Invariant Face Recognition

Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions

Ms. Ying Liu | Surveillance | Best Researcher Award

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Ms. Ying Liu | Surveillance | Best Researcher Award

Ms. Ying Liu, Northeast Forestry University, China

Jian Xing is currently pursuing a Master’s degree in Computer and Control Engineering at Northeast Forestry University, where he also completed his Bachelor’s degree. His research interests primarily focus on the application of UAV technology in monitoring and enhancing infrastructure, particularly in the field of highway construction. Jian has been actively involved in several notable research projects, including the “Intelligent Monitoring Application of UAV in the Process of Highway Construction,” supported by the Heilongjiang Province Transportation Department’s Research Project and the Science and Technology Project of Heilongjiang Transportation Department (HJK2023B019). He has also contributed to the National Natural Science Foundation project titled “Study on the Prediction Method of Small Dead Fuel Moisture Content of Forest Surface by Land-Space Semi-Physical Model” (Project Number: 32371864). Jian Xing has co-authored several impactful papers, including “Improved YOLOV5-Based UAV Pavement Crack Detection” published in IEEE Sensors Journal (IF: 4.3), “Based on the UAV Cross-Modal YOLOV5 Slope Crack Detection” in Sensors, and “UAV Multispectral Imagery Predicts Dead Fuel Moisture Content” in Forests (IF: 2.9). His research contributions highlight his expertise in UAV-based monitoring systems and their applications in infrastructure and environmental management.

Professional Profile

šŸ‘Øā€šŸŽ“ Education:

  • 2022.09-今: Master’s in Computer and Control Engineering, Northeast Forestry University
  • 2016.09-2020.06: Bachelor’s in Computer and Control Engineering, Northeast Forestry University

šŸ”¬ Research Focus:

  • Jian Xing has been actively involved in several research projects, including:
    • Research Project of Heilongjiang Province Transportation Department on intelligent monitoring application of UAV in highway construction (Science and Technology Project of Heilongjiang Transportation Department, HJK2023B019)
    • National Natural Science Foundation Project on predicting small dead fuel moisture content of forest surfaces using a land-space semi-physical model (Project number: 32371864)

šŸ“„ Publications:

  • Jian Xing has contributed to the following publications:
    • “Improved YOLOV5-Based UAV Pavement Crack Detection” in IEEE Sensors Journal (Impact Factor: 4.3)
    • “Based on the UAV cross-modal YOLOV5 slope crack detection” in Sensors
    • “UAV multispectral imagery predicts dead fuel moisture content” in Forests (Impact Factor: 2.9)

šŸŒŸ Achievements:

  • His work has significantly advanced the field of UAV-based pavement and slope crack detection, as well as remote sensing applications in predicting moisture content.

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Publications Notes:šŸ“„

Concrete Highway Crack Detection Based on Visible Light and Infrared Silicate Spectrum Image Fusion

UAV Multispectral Imagery Predicts Dead Fuel Moisture Content

Improved YOLOV5-Based UAV Pavement Crack Detection