Prof. Khaled Shaban | Data Science | Best Researcher Award

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Prof. Khaled Shaban | Data Science | Best Researcher Award

Prof. Khaled Shaban, Qatar University, Qatar

Prof. Khaled Shaban is a distinguished researcher and professor in Computer Science and Engineering at Qatar University. With expertise in Computational Intelligence, Machine Learning, and Data Science, he has significantly contributed to advancing pattern recognition, cloud computing, and cybersecurity. A senior member of IEEE and ACM, he has received multiple accolades for his groundbreaking research. He also holds an adjunct professorship at the University of Waterloo, reinforcing his global academic influence. His work focuses on AI-driven disease prediction, smart systems, and optimization techniques, making him a leader in intelligent computing innovations.

๐ŸŒย Professional Profile:

Google Scholar

Orcid

Scopus

๐Ÿ† Suitability for Best Researcher Award

Prof. Khaled Shabanโ€™s research excellence, innovative contributions, and global recognition make him an ideal candidate for the Best Researcher Award. His pioneering work in Machine Learning, AI, and Computational Intelligence has led to influential publications and prestigious awards, such as the Best Paper Award at IRICT 2021. His ability to merge theory and application in AI, cloud computing, and cybersecurity has significantly impacted academia and industry. His leadership in top-tier conferences and IEEE/ACM communities underscores his commitment to advancing knowledge, making him a highly deserving candidate for this distinguished recognition.

๐ŸŽ“ Education

Prof. Khaled Shaban holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada (2006), specializing in Pattern Recognition and Machine Intelligence. His academic journey began with an M.Sc. in Engineering Systems and Computing (2002) from the University of Guelph, Canada, where he developed a strong foundation in computational intelligence and optimization. His interdisciplinary education has enabled him to integrate machine learning, data science, and engineering systems into cutting-edge research. His expertise in algorithms and computing theory has positioned him as a global leader in AI and intelligent systems research.

๐Ÿ’ผ Experience

Prof. Khaled Shaban has an extensive academic career, currently serving as a Professor at Qatar Universityโ€™s College of Engineering (since April 2021). He previously held roles as Associate Professor (2016-2021) and Assistant Professor (2008-2016). Additionally, he is an Adjunct Professor at the University of Waterloo (2021-2027), collaborating on AI-driven computing innovations. His professional affiliations with IEEE, ACM, and international research communities enhance his impact on global technological advancements. Over the years, he has mentored numerous students and led transformative research in Artificial Intelligence, Data Science, and Optimization.

๐Ÿ… Awards & Honors

  • ๐Ÿ† Best Paper Award โ€“ IRICT 2021 for “C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques”
  • ๐Ÿ… Nomination for Best Paper Award โ€“ ICVS 2021 for “MARL: Multimodal Attentional Representation Learning for Disease Prediction”
  • ๐ŸŽ– Promoted to Professor โ€“ Qatar University, 2021
  • ๐Ÿ”ฌ Senior Member, IEEE & ACM โ€“ Recognized for contributions to AI and Computational Intelligence
  • ๐ŸŒ International Collaborations โ€“ Adjunct Professor at the University of Waterloo, fostering global research partnerships

๐Ÿ”ฌ Research Focus

Prof. Khaled Shabanโ€™s research lies at the intersection of Artificial Intelligence, Computational Intelligence, and Data Science. His work in Machine Learning-driven healthcare analytics, particularly in disease prediction and medical image analysis, is widely recognized. He has also made significant contributions to cybersecurity, cloud computing, and smart grid systems. His studies on optimization and knowledge discovery enhance IoT, AI-based automation, and intelligent computing solutions. Through numerous publications and projects, he has addressed real-world challenges in AI, energy-efficient computing, and adaptive learning systems, making his research impactful across academia and industry.

๐Ÿ“–ย Publication Top Notes

  • Urban Air Pollution Monitoring System with Forecasting Models

    • Year: 2016
    • Citations: 341

  • Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends

    • Year: 2016
    • Citations: 321

  • Delay-Aware Scheduling and Resource Optimization with Network Function Virtualization

    • Year: 2016
    • Citations: 266

  • A Reliability-Aware Network Service Chain Provisioning with Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks

    • Year: 2017
    • Citations: 224

  • Deep Learning Models for Sentiment Analysis in Arabic

    • Year: 2015
    • Citations: 150

 

 

Mr. Mohammad Mahdi Badami | Data Analysis | Young Scientist Award

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Mr. Mohammad Mahdi Badami | Data Analysis | Young Scientist Award

Mr. Mohammad Mahdi Badami | University of Southern California | United States

Mehdi Badami is a dedicated Ph.D. candidate in Environmental Engineering at the University of Southern California (USC) under Prof. Constantinos Sioutas. His expertise lies in air quality improvement, with hands-on experience in air pollution monitoring using advanced instrumentation such as SMPS-CPC, OPS, and Aethalometer 51. He specializes in data-driven environmental assessments, employing Python for pollution source apportionment and emission trend analysis. His research contributes to community-centric environmental policies and sustainable air quality solutions. Passionate about environmental justice, he aims to bridge scientific research with real-world policy implementation. ๐ŸŒฑ๐Ÿ”ฌ

Professional Profile:

Google Scholar

Suitability for the Young Scientist Award

Mehdi Badami is a strong candidate for the Young Scientist Award due to his significant contributions to environmental engineering, particularly in air quality improvement. As a Ph.D. candidate at the University of Southern California (USC), his research focuses on air pollution monitoring and data-driven environmental assessments. His expertise in advanced instrumentation (e.g., SMPS-CPC, OPS, Aethalometer 51) and Python-based pollution source apportionment makes him a valuable asset to the field.

Education & Experience ๐Ÿข๐ŸŽ“

  • Ph.D. Candidate in Environmental Engineering (2022-Present) โ€“ USC, Los Angeles, USA ๐Ÿ‡บ๐Ÿ‡ธ

    • GPA: 3.95/4
    • Advisor: Prof. Constantinos Sioutas

  • M.Sc. in Mechanical Engineering (Fluid Mechanics) (2017-2020) โ€“ University of Tehran, Iran ๐Ÿ‡ฎ๐Ÿ‡ท

    • GPA: 3.77/4
    • Supervisors: Dr. Alireza Riasi, Prof. Kayvan Sadeghy

  • B.Sc. in Mechanical Engineering (2012-2016) โ€“ K. N. Toosi University of Technology, Iran ๐Ÿ‡ฎ๐Ÿ‡ท

  • Research Assistant โ€“ USC Aerosol Lab (2023โ€“Present) ๐Ÿญ๐ŸŒซ๏ธ

    • Conducted air pollution measurements using state-of-the-art monitoring systems
    • Developed Python programs for data automation and pollution trend analysis
    • Led collaborations with institutions like Harvard, UCLA, and Dresden University
    • Mentored Ph.D. students on environmental research projects

  • Research Assistant โ€“ Hydro-kinetic Energy Lab, University of Tehran (2017โ€“2022) ๐Ÿ”ฌ๐Ÿ’ง

    • Investigated fluid mechanics phenomena related to blood hammer effects in arteries

  • Teaching Assistant โ€“ USC & University of Tehran (2018โ€“2024) ๐Ÿ“š๐Ÿ‘จโ€๐Ÿซ

    • Assisted in courses on climate change, air quality, fluid mechanics, and thermodynamics

Professional Development ๐Ÿš€

Mehdi Badami has actively contributed to the field of environmental engineering through cutting-edge research on air pollution, sustainability, and emission control. His extensive knowledge of aerosol science, atmospheric chemistry, and data analysis allows him to assess air quality trends with precision. He has developed innovative models for pollution source apportionment, worked on real-time monitoring systems, and collaborated with leading institutions to improve urban air quality. His passion for environmental justice drives his work towards creating actionable solutions that ensure healthier air for communities. His dedication extends beyond academia, as he actively engages in outreach and policy-driven initiatives. ๐ŸŒฟ๐Ÿ“Š

Research Focus ๐Ÿ”

Mehdiโ€™s research centers on air pollution control, environmental monitoring, and sustainable urban development. His work involves identifying and mitigating pollution sources through field measurements and computational models. He specializes in:

  • Air Quality Assessment ๐ŸŒซ๏ธ๐Ÿ“Š โ€“ Studying PM2.5 and ultrafine particle pollution in urban environments
  • Pollution Source Apportionment ๐Ÿญโš–๏ธ โ€“ Analyzing emissions from vehicles, industries, and natural sources
  • Aerosol Science ๐ŸŒช๏ธ๐Ÿ’จ โ€“ Investigating the toxicity and health impacts of airborne particles
  • Machine Learning in Environmental Studies ๐Ÿค–๐Ÿ“‰ โ€“ Utilizing data science to model pollution trends
  • Climate and Environmental Justice ๐ŸŒŽโš–๏ธ โ€“ Advocating for equitable air quality policies in urban communities

Awards & Honors ๐Ÿ†

  • Outstanding Research Assistant Award โ€“ USC, Sonny Astani Department of Civil and Environmental Engineering (2024) ๐Ÿ…
  • Fellowship Award โ€“ USC (2022-2023) ๐ŸŽ“๐Ÿ’ฐ (Recognized for academic excellence in Environmental Engineering)
  • National Fellowship for Masterโ€™s Studies โ€“ University of Tehran (2017) ๐Ÿ“–๐Ÿ†
  • Top 0.15% Rank in National Entrance Exam โ€“ Iran (Competitive ranking in Mechanical Engineering)

Publication Top Notes:

๐Ÿ“„ Design, optimization, and evaluation of a wet electrostatic precipitator (ESP) for aerosol collection โ€“ Atmospheric Environment (2023) โ€“ ๐Ÿ“‘ Cited by: 11
๐Ÿ“„ Size-segregated source identification of water-soluble and water-insoluble metals and trace elements of coarse and fine PM in central Los Angeles โ€“ Atmospheric Environment (2023) โ€“ ๐Ÿ“‘ Cited by: 7
๐Ÿ“„ Numerical study of blood hammer phenomenon considering blood viscoelastic effects โ€“ European Journal of Mechanics-B/Fluids (2022) โ€“ ๐Ÿ“‘ Cited by: 7
๐Ÿ“„ Development and performance evaluation of online monitors for near real-time measurement of total and water-soluble organic carbon in fine and coarse ambient PM โ€“ Atmospheric Environment (2024) โ€“ ๐Ÿ“‘ Cited by: 4
๐Ÿ“„ Numerical analysis of laminar viscoelastic fluid hammer phenomenon in an axisymmetric pipe โ€“ Journal of the Brazilian Society of Mechanical Sciences and Engineering (2021) โ€“ ๐Ÿ“‘ Cited by: 3
๐Ÿ“„ Urban emissions of fine and ultrafine particulate matter in Los Angeles: Sources and variations in lung-deposited surface area โ€“ Environmental Pollution (2025) โ€“ ๐Ÿ“‘ Cited by: 1

 

 

 

Fan Wang | Data Analysis | Best Researcher Award

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Mrs. Fan Wang | Data Analysis | Best Researcher Award

Mrs. Fan Wang, Xi’an Shiyou University, China .

Mrs. Fan Wang is a Lecturer at Xiโ€™an Shiyou University, China, specializing in imaging, image processing, data analysis, and machine learning. She earned her Ph.D. and Masterโ€™s degrees in Graphic and Image Processing from Northwestern Polytechnical University, Xi’an, China. With a strong academic foundation, Dr. Wang is passionate about advancing methodologies in image processing and applying machine learning to solve complex visual data challenges. Her expertise in data-driven approaches continues to inspire innovation and impactful contributions to the field of computational imaging. ๐Ÿ’ป๐Ÿ“Š

Publication Profile

Scopusย ๐Ÿ“š

Education and Experience

Education
  • Ph.D. in Theory and Methods of Graphic and Image Processing, Northwestern Polytechnical University, Xi’an, China (2018โ€“2022)ย ๐ŸŽ“
  • M.S. in Theory and Methods of Graphic and Image Processing, Northwestern Polytechnical University, Xi’an, China (2015โ€“2018)

Experience

  • Lecturer, Xiโ€™an Shiyou University, Xiโ€™an, China (2022โ€“present)ย ๐ŸŽ“๐Ÿ“

Suitability for the Award

Mrs. Fan Wang, a dedicated researcher and Lecturer at Xiโ€™an Shiyou University, specializes in imaging, image processing, data analysis, and machine learning. With a Ph.D. and M.S. in Theory and Methods of Graphic and Image Processing from Northwestern Polytechnical University, she has demonstrated expertise in advanced computational techniques. Her contributions to innovative research and academic excellence make her a strong contender for the Best Researcher Award.ย ๐Ÿ†

Professional Development

Mrs. Fan Wang is a researcher and educator specializing in cutting-edge techniques in imaging and machine learning. With a Ph.D. in Graphic and Image Processing, she has developed advanced skills in data analysis and the application of AI algorithms to enhance image interpretation and processing. Currently a Lecturer at Xiโ€™an Shiyou University, Dr. Wang is committed to fostering innovation and knowledge dissemination through teaching and collaborative research. Her work integrates computational intelligence with visual data, advancing impactful solutions in imaging technologies. ๐ŸŒฑ๐Ÿ’ก

Research Focus

Mrs. Fan Wang’s research lies at the intersection of imaging and artificial intelligence. She focuses on developing innovative methods for image processing, leveraging data analysis to optimize the extraction of meaningful information from complex visual datasets. Her work also involves applying machine learning techniques to automate and enhance image interpretation for diverse applications. Dr. Wang aims to address challenges in computational imaging by combining theory with practical solutions, driving advancements in visualization technologies for academic and industrial use.ย ๐Ÿ”๐Ÿค–

Awards and Honors

  • Ph.D. Scholarship Award, Northwestern Polytechnical University (2022)ย ๐Ÿ…
  • Recognized for Excellence in Research during Graduate Studies (2018โ€“2022)
  • Best Presentation Award in Machine Learning Symposium (2021)ย ๐Ÿ†
  • Published high-impact research in top-tier journals on imaging and AI methods
  • Contributor to innovative methodologies in graphic and image processing

Publication Highlights

  • ๐Ÿ“–ย Intensifying graph diffusion-based salient object detection with sparse graph weightingย (2023) โ€“ Cited by: 0
  • ๐Ÿ“–ย Graph construction by incorporating local and global affinity graphs for saliency detectionย (2022) โ€“ Cited by: 3
  • ๐Ÿ“–ย Saliency detection based on color descriptor and high-level priorย (2021) โ€“ Cited by: 3
  • ๐Ÿ“–ย Graph-based saliency detection using a learning joint affinity matrixย (2021) โ€“ Cited by: 4
  • ๐Ÿ“–ย Saliency detection via coarse-to-fine diffusion-based compactness with weighted learning affinity matrixย (2021) โ€“ Cited by: 1
  • ๐Ÿ“–ย Salient object detection via cross diffusion-based compactness on multiple graphsย (2021) โ€“ Cited by: 4
  • ๐Ÿ“œย Salient Object Detection via Quaternionic Local Ranking Binary Pattern and High-Level Priorsย (2019, Conference Paper) โ€“ Cited by: 0
  • ๐ŸŒŠย Underwater Image Restoration Based on Background Light Estimation and Dark Channel Priorย (2018) โ€“ Cited by: 25

Elena Zaitseva | Data Mining | Best Researcher Award

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Elena Zaitseva | Data Mining | Best Researcher Award

Prof. Dr. Elena Zaitseva, University of Zilina , Slovakia.

Publication profile

Scopus
Googlscholar
Orcid

Education And Experiance

  • ๐ŸŽ“ย MSc in Computer Scienceย (1989) โ€“ Radioengineering Institute, Minsk, Belarus.
  • ๐ŸŽ“ย Ph.D. in Computer Scienceย (1994) โ€“ State University of Informatics and Radioelectronics, Belarus.
  • ๐ŸŽ“ย Associate Professor in Applied Informaticsย (1998) โ€“ Belarus State Economic University.
  • ๐ŸŽ“ย Professor in Applied Informaticsย (2015) โ€“ University of ลฝilina, Slovakia.
  • ๐Ÿ‘ฉโ€๐Ÿซย Teaching: Courses on Applied Informatics, C++, Neural Networks, Reliability Analysis, and Decision-Making Systems.
  • ๐Ÿง‘โ€๐Ÿ’ปย Research: Focus on multiple-valued logic, reliability analysis, and data mining applications.

Suitability For The Award

Prof. Dr. Elena Zaitseva is an exceptionally qualified candidate for the Best Researcher Award due to her remarkable academic career, innovative contributions to multiple research domains, and leadership roles in international scientific communities. With over three decades of professional experience, she has made significant advancements in applied informatics, reliability analysis, and multiple-valued logic, among other fields. Her work seamlessly bridges theoretical research and practical applications, particularly in data mining, healthcare reliability, and decision support systems.

Professional Developmentย 

๐ŸŒย Elena Zaitsevaย is a prominent member of various international organizations, including theย Gnedenko Forumย andย IEEE Czechoslovakia Section Reliability Society, where she chairs significant committees. She has been co-editor and editorial board member for several journals, such asย Mathematical Problems in Engineeringย andย Innovative Technologies and Scientific Solutions for Industries. Her leadership extends to steering technical chapters inย European Safety and Reliability Association (ESRA). Through her dedication to professional excellence, she mentors researchers worldwide, advances computational reliability, and fosters interdisciplinary collaboration. Her innovative spirit is reflected in her contributions to the reliability and biomedical informatics communities.ย ๐ŸŒŸ

Research Focusย 

Awards and Honors

  • ๐Ÿ†ย Chairย of IEEE Czechoslovakia Section Reliability Society Chapter (2018 โ€“ Present).
  • ๐ŸŽ–๏ธย Chairย of ESRA Technical Chapter on Information Technologies and Communication (2011 โ€“ Present).
  • ๐Ÿ“œย Memberย of Editorial Boards for numerous international journals, includingย CERESย andย Mathematical Problems in Engineering.
  • ๐Ÿ…ย Recognized for leadership inย Gnedenko Forumย and European safety initiatives.
  • ๐ŸŒŸย Renowned for her impactful contributions toย reliability and statistical studiesย in academia and industry.

Publoication Top Notes

  • Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities, and challengesย (Cited by: 173, Year: 2022)ย ๐ŸŒŸ๐Ÿค–
  • Construction of a reliability structure function based on uncertain dataย (Cited by: 93, Year: 2016)ย ๐Ÿ“Š๐Ÿ”
  • Reliability analysis of multi-state system with application of multiple-valued logicย (Cited by: 84, Year: 2017)ย โš™๏ธ๐Ÿงฎ
  • Review of some applications of unmanned aerial vehicles technology in the resource-rich countryย (Cited by: 70, Year: 2021)ย ๐Ÿš๐ŸŒ
  • Multiple-valued logic mathematical approaches for multi-state system reliability analysisย (Cited by: 66, Year: 2013)ย ๐Ÿ”ข๐Ÿ“
  • Importance analysis by logical differential calculusย (Cited by: 65, Year: 2013)ย ๐Ÿ“–โšก
  • A review of continuous authentication using behavioral biometricsย (Cited by: 59, Year: 2016)ย ๐Ÿ–ฅ๏ธ๐Ÿ”‘