Gholamreza Hesamian | Fuzzy Statistical Analysis | Best Researcher Award

Gholamreza Hesamian | Fuzzy Statistical Analysis | Best Researcher Award

Prof. Gholamreza Hesamian, Payame Noor University, Iran

Dr. Gholamreza Hesamian ๐ŸŽ“ is an accomplished Iranian statistician specializing in fuzzy statistical modeling and imprecise data analysis. Born on March 21, 1979, in Isfahan ๐Ÿ‡ฎ๐Ÿ‡ท, he currently serves at the Department of Statistics, Payame Noor University, Tehran ๐Ÿ“. With a Ph.D. in Statistics from Isfahan University of Technology, his work focuses on integrating fuzzy mathematics ๐Ÿค– into statistical inference. His research offers robust solutions in uncertain environments, making valuable contributions to modern statistical science ๐Ÿ“Š. Dr. Hesamian is dedicated to teaching, research, and academic development, helping shape the next generation of data scientists and statisticians ๐Ÿ‘จโ€๐Ÿซโœจ.

Profile :

๐ŸŽ“Education & Experience :

Dr. Gholamreza Hesamian ๐ŸŽ“ began his academic journey with a B.S. in Statistics from Isfahan University in 2000. He continued his studies at Isfahan University of Technology, earning an M.A. in Statistics ๐Ÿ“˜ in 2004, followed by a Ph.D. in Statistics ๐Ÿ“š in 2012. His doctoral research focused on Non-Parametric Statistical Inference based on Imprecise Information ๐Ÿ“, under the supervision of Dr. S.M. Taheri ๐Ÿ‘จโ€๐Ÿซ. Currently, he serves as a faculty member at the Department of Statistics, Payame Noor University in Tehran ๐Ÿข, where he teaches and conducts research in advanced statistical modeling and fuzzy mathematics.

๐Ÿ“š Professional Development :

Dr. Hesamian has consistently advanced his academic and professional development ๐Ÿ“ˆ through dedicated research, publication, and active engagement in statistical education. He has participated in academic conferences ๐ŸŽค, contributed to scientific journals ๐Ÿ“‘, and collaborated on interdisciplinary projects involving fuzzy logic and probability ๐Ÿค. His commitment to knowledge-sharing and curriculum innovation enhances statistical learning outcomes ๐Ÿ’ผ๐Ÿ“˜. Through workshops, seminars, and mentorship, he inspires students and young researchers to pursue excellence in data science, especially under conditions of uncertainty ๐ŸŽฏ๐Ÿง . Dr. Hesamian remains a proactive contributor to Iranโ€™s academic and research community in statistics ๐Ÿ‡ฎ๐Ÿ‡ท.

๐Ÿ”ฌ Research Focus :

Dr. Hesamianโ€™s research focuses on fuzzy statistics ๐Ÿค–๐Ÿ“Šโ€”a field that combines traditional statistical techniques with fuzzy logic to model uncertainty and imprecise information. His work enhances data analysis when classical probabilistic models fall short, particularly in decision-making environments marked by vagueness and ambiguity ๐ŸŒซ๏ธ๐Ÿ”. Key areas include fuzzy probability, fuzzy mathematical models, and non-parametric inference under uncertainty ๐Ÿ“ˆ. His research has wide applications in engineering, economics, and social sciences where exact data is difficult to obtain ๐Ÿงฎ๐Ÿ’ก. By bridging mathematics and real-world complexity, Dr. Hesamian contributes significantly to the development of intelligent and adaptive data systems ๐Ÿค“โš™๏ธ.

๐Ÿ† Awards and Honors :

Dr. Gholamreza Hesamian ๐Ÿ… has been recognized for his outstanding Ph.D. research in Non-Parametric Inference with Imprecise Data. He has received honors ๐ŸŽ–๏ธ from Payame Noor University for Excellence in Research and has been commended ๐Ÿ“œ for his significant academic contributions to Fuzzy Statistical Modeling. Additionally, he has actively participated ๐Ÿ† in national conferences focused on Advanced Statistics and Fuzzy Systems, showcasing his dedication to advancing the field.

๐Ÿ”นPublication Top Notes :

1. A Fuzzy Multiple Regression Model Adopted with Locally Weighted and Interval-Valued Techniques
  • Authors: Gholamreza Hesamian, Arne Johannssen, Nataliya Chukhrova

  • Journal: Journal of Computational and Applied Mathematics

  • Year: 2026

  • Type: Open Access

  • Summary:
    This study introduces a fuzzy multiple regression model that integrates locally weighted regression with interval-valued fuzzy techniques. The proposed method addresses uncertainties in predictor-response relationships and improves interpretability in fuzzy environments. Local weighting enables the model to adapt flexibly to localized data patterns, while interval-valued fuzzy numbers help handle imprecise or vague data.

2. A Two-way Crossed Effects Fuzzy Panel Linear Regression Model
  • Authors: Gholamreza Hesamian, Arne Johannssen

  • Journal: International Journal of Computational Intelligence Systems

  • Year: 2025

  • Volume: 18, Issue 1, Article 13

  • Type: Open Access

  • Summary:
    This article proposes a fuzzy panel data regression model that incorporates two-way crossed random effects, capturing both individual and time-related variability. The fuzzy framework accommodates vagueness in longitudinal data, improving forecasting and inference in applications where uncertainty is prominent, such as economics and social sciences.

3. A Fuzzy Multivariate Regression Model to Control Outliers and Multicollinearity Based on Exact Predictors and Fuzzy Responses
  • Authors: Gholamreza Hesamian, Mohammad Ghasem H. Akbari, Mehdi Shams

  • Journal: Iranian Journal of Mathematical Sciences and Informatics

  • Year: 2025

  • Summary:
    This model introduces a fuzzy multivariate regression approach designed to handle outliers and multicollinearity in regression analysis. It uses exact numerical predictors and fuzzy-valued responses to provide robust estimation and reduce the effect of anomalies or correlated variables, especially useful in uncertain data settings like finance or environmental studies.

4. A Flexible Soft Nonlinear Quantile-Based Regression Model
  • Authors: Gholamreza Hesamian, Arne Johannssen, Nataliya Chukhrova

  • Journal: Fuzzy Optimization and Decision Making

  • Year: 2025

  • Type: Open Access

  • Summary:
    This article introduces a nonlinear soft regression model based on quantile estimation techniques in fuzzy environments. It allows modeling asymmetric distributions and tail behaviors under uncertainty. This is particularly useful in decision-making and risk assessment where traditional mean-based models fail to capture distributional extremes.

5. A Neural Network-Based ARMA Model for Fuzzy Time Series Data
  • Authors: Gholamreza Hesamian, Arne Johannssen, Nataliya Chukhrova

  • Journal: Computational and Applied Mathematics

  • Year: 2024

  • Summary:
    Combines ARMA (AutoRegressive Moving Average) models with neural networks for modeling fuzzy time series. This hybrid model handles both temporal dependencies and fuzzy uncertainty, offering improved accuracy in forecasting complex real-world systems such as energy demand or economic indicators.

๐Ÿ”นConclusion:

Given his trailblazing contributions to fuzzy statistical inference, commitment to academic excellence, and influence on the next generation of researchers, Dr. Gholamreza Hesamian embodies the values and vision of the Best Researcher Award. His work not only enhances statistical science but also provides vital tools for real-world decision-making under uncertainty. He is a deserving recipient of this recognition.

Prof. Khaled Shaban | Data Science | Best Researcher Award

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

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

 

 

 

Tayfun Abut | Methods and Algorithms | Best Researcher Award

Assist Prof. Tayfun Abut | Methods and Algorithms | Best Researcher Award

Assist Prof Dr. Tayfun Abut, Mus Alparslan University, Turkey

Dr. Tayfun Abut is an Assistant Professor of Mechanical Engineering at Mus Alparslan University. He earned his Doctoral and Master’s degrees from Firat University, where he also completed his Bachelor’s degree. Dr. Abut has held various academic and leadership positions, including Vice Dean and Head of Major Department, contributing significantly to his institution’s academic and administrative functions. His research focuses on control systems, haptic teleoperation, and dynamic analysis of mechanical systems, with numerous publications in reputable journals. Dr. Abut’s work has earned him several honors, including the Highly Commended Paper Award from Emerald Publishing and TรœBฤฐTAK’s Publication Incentive Awards. He is dedicated to continuous learning, actively participating in workshops and training to further his expertise.

๐ŸŒ Professional Profile:

ORCIDย 
Scopus

๐ŸŽ“ Educational Background:

Dr. Tayfun Abut earned his Doctoral degree from Firat University’s Institute of Science on March 10, 2022. He previously obtained a Master’s degree (Thesis) from the same institution on August 27, 2015, and completed his Bachelor’s degree on June 15, 2012. His academic journey has been rooted in Firat University, where he has built a strong foundation in Mechanical Engineering.

๐Ÿ’ผย Experience:

Dr. Abut has a diverse range of academic positions. He started as a Research Assistant at Firat University’s Faculty of Engineering, specializing in Mechanical Theory and Dynamics. He continued his career at Mus Alparslan University, serving as a Research Assistant in the Department of Mechanical Engineering, focusing on System Dynamics and Control. Since August 5, 2022, Dr. Abut has been an Assistant Professor at Mus Alparslan University in the Department of Mechanical Engineering.

๐Ÿ“š Workshops and Training:

Dr. Abut’s work has been recognized with several honors and awards. Notably, he received the Highly Commended Paper Award from Emerald Publishing in 2020. He has also been awarded Publication Incentive Awards from TรœBฤฐTAK in 2016 and 2017, highlighting his contributions to research and academic excellence.

๐Ÿ… Honors and Awards:

She has received several accolades, including the Outstanding Graduate Award from the School of International Education, Dalian University of Technology (2024), the DUT International Students Presidential Scholarship (full scholarship), and the Youth Star Award (2022). She also earned the Best Teacher Award for the 2018-2019 session from Sir Syed School, Wah Cantt, Pakistan, and a merit scholarship for her top performance in her MS and BS programs.

Publication Top Notes:

  • Real-time control and application with self-tuning PID-type fuzzy adaptive controller of an inverted pendulum
    • Year: 2019
    • Citations: 31
  • Haptic industrial robot control and bilateral teleoperation by using a virtual visual interface | Sanal bir gรถrsel arayรผz kullanarak haptik endรผstriyel robot kontrolรผ ve iki yรถnlรผ teleoperasyon
    • Year: 2018
    • Citations: 6
  • Real-time control of bilateral teleoperation system with adaptive computed torque method
    • Year: 2017
    • Citations: 10
  • Haptic industrial robot control with variable time delayed bilateral teleoperation
    • Year: 2016
    • Citations: 18
  • Motion control in virtual reality based teleoperation system | Sanal Gerรงeklik Tabanlฤฑ Teleoperasyon Sisteminde Hareket Kontrolรผ
    • Year: 2015
    • Citations: 2