Dr. Peng Zhi | Deep Learning | Best Researcher Award

Posted on 07/02/2025 by ScienceFather

Dr. Peng Zhi | Deep Learning | Best Researcher Award

Dr. Peng Zhi, Lanzhou University, China

Peng Zhi is a Ph.D. candidate in Computer Science at Lanzhou University, China, specializing in computer vision, deep learning, and autonomous driving. He earned his Bachelor’s and Master’s degrees in Computer Science and Technology from Lanzhou University in 2017 and 2020, respectively. His research focuses on LiDAR-camera fusion, 3D object detection, and AI applications in intelligent transportation systems. He has published several high-impact papers in renowned journals and conferences, contributing to advancements in autonomous vehicle perception and artificial intelligence. Additionally, he has co-authored the book Theories and Practices of Self-Driving Vehicles, further solidifying his expertise in the field.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award

Peng Zhi is a strong candidate for the Best Researcher Award, given his innovative contributions to computer vision, deep learning, and autonomous driving. As a Ph.D. candidate at Lanzhou University, he has been actively involved in research that enhances LiDAR-based 3D object detection, cross-domain generalization, and deep learning applications in autonomous systems.

🎓 Education

Ph.D. in Computer Application Technology (2021 – Present)
Lanzhou University, Lanzhou, China
Master’s in Computer System Architecture (2017 – 2020)
Lanzhou University, Lanzhou, China
Bachelor’s in Computer Science and Technology (2013 – 2017)
Lanzhou University, Lanzhou, China

💼 Work Experience

Ph.D. Candidate & Researcher (2021 – Present)
Lanzhou University, Lanzhou, China
- Conducts advanced research in computer vision, deep learning, and autonomous driving
- Publishes in top-tier journals and conferences
- Develops LiDAR and camera fusion models for 3D object detection

🏆 Achievements & Contributions

Published Multiple Research Papers 📄 in top journals and conferences, including Tsinghua Science and Technology, Electronic Research Archive, and IEEE ITSC
Author of a Book on Self-Driving Vehicles 📘 Theories and Practices of Self-Driving Vehicles (Elsevier, 2022)
Developed DefDeN Model 🤖 A deformable denoising-based LiDAR and camera feature fusion model for 3D object detection
Research on Autonomous Driving 🚗 Focused on boundary distribution estimation and cross-domain generalization for LiDAR-based 3D object detection

🏅 Awards & Honors

Best Paper Award 🏆 at an International Conference on Intelligent Transportation Systems (ITSC)
Outstanding Researcher Award 🎖️ at Lanzhou University for contributions to AI and autonomous driving
National Scholarship 🏅 for academic excellence in computer science and AI research

Publication Top Notes:

Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle Environments

Ahmet Güneyli | Artificial intelligence Awards | Best Researcher Award

Posted on 03/01/202503/01/2025 by ScienceFather

Prof. Dr. Ahmet Güneyli | Artificial intelligence Awards | Best Researcher Award

Prof. Dr. Ahmet Güneyli, European University of Lefka, Cyprus.

Ahmet GÜNEYLİ is a Professor of Turkish Language Teaching at the European University of Lefke in North Cyprus. With over two decades of academic experience, he has made significant contributions to Turkish language education, educational sciences, and teacher training. His career spans roles from Assistant Professor at Near East University to his current position as Professor. He supervises numerous master’s and Ph.D. theses, fostering research in multilingual education, instructional strategies, and educational management. Prof. GÜNEYLİ is an active participant in academic publishing and conference presentations, establishing himself as a leading figure in his field.

Professional Profile

Scopus

ORCID

Researcher Suitability Summary for Best Researcher Award

Professor Ahmet Güneyli exhibits exceptional academic and research credentials, positioning him as a strong candidate for the Research for Best Researcher Award. His scholarly achievements, significant contributions to Turkish language education, and commitment to mentoring young researchers underscore his suitability for this recognition.

Education 🎓

Undergraduate: Bachelor’s in Preschool & Primary School Education, Teachers Training College, Cyprus (2000).
Master’s: M.A. in Educational Sciences with a focus on Turkish Language Teaching, Ankara University, Turkey (2003).
Ph.D.: Educational Sciences specializing in Turkish Language Teaching, Ankara University, Turkey (2007).

Ahmet’s academic journey reflects his dedication to Turkish language education and instructional methodologies. His rigorous training has equipped him to address complex educational challenges effectively.

Experience 💼

Ahmet began his career as an Assistant Professor at Near East University in 2009, rising to Associate Professor in 2015. By 2021, he achieved full professorship at the European University of Lefke. His leadership includes supervising theses on multilingual education, bilingual instructional methods, and organizational analysis in education. His work combines practical applications with theoretical frameworks, enhancing education quality in Northern Cyprus.

Research Interests 🔬

Prof. GÜNEYLİ focuses on Turkish language education, bilingual instructional methods, and educational program evaluation. His interdisciplinary approach integrates educational sciences with language studies, aiming to advance instructional techniques and organizational efficiency in schools. His research supports inclusive and multilingual education policies.

Awards 🏆

Ahmet has earned numerous awards for his contributions to Turkish language teaching and educational sciences, underscoring his academic and professional excellence. These honors recognize his innovative teaching methods, impactful research, and dedication to advancing education.

Publications Top Notes 📚

Understanding University Students’ Foreign Language Learning Attitudes: An Analysis Based on Stereotypes

Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus

Turkish Language Teachers’ Perspectives on Listening Skills Education in Turkey and Northern Cyprus

The effectiveness of virtual reality-based technology on foreign language vocabulary teaching to children with attention deficiency hyperactivity disorder

Examining Conjoint Behavioral Consultation to Support 2e-Autism Spectrum Disorder and Gifted Students in Preschool with Academic and Behavior Concerns

Akmal Jahan Mohamed Abdul Cader | Artificial Intelligence | Best Researcher Award

Posted on 28/12/202428/12/2024 by ScienceFather

Akmal Jahan Mohamed Abdul Cader | Artificial Intelligence | Best Researcher Award

Dr. Akmal Jahan Mohamed Abdul Cader, South Eastern University, Sri Lanka.

Dr. Akmal Jahan Mohamed Abdul Cader is a distinguished academic and researcher currently serving as a Senior Lecturer in Computer Science at the South Eastern University of Sri Lanka. With extensive experience in higher education, he is a Visiting Research Fellow at QUT, Australia. His research interests include artificial intelligence, data science, and document image analysis. Dr. Cader has published numerous high-impact articles and is actively involved in academic development and curriculum design. He is committed to advancing education and research in the field of computer science.

Publication Profiles

Googlescholar

Education and Experience

Visiting Research Fellow – QUT Momentum Visiting Fellow, QUT, Australia (2021 – Present)
Senior Lecturer (Computer Science) – South Eastern University of Sri Lanka (2020 – Present)
Sessional Academic – School of Electrical Engineering & Computer Science, QUT (2016 – 2019)
Lecturer (Computer Science) – South Eastern University of Sri Lanka (2012 – 2015)
Assistant Lecturer – South Eastern University of Sri Lanka (2010 – 2012)
Demonstrator in Computer Science – South Eastern University of Sri Lanka (2009 – 2010)

Suitability For The Award

Dr. Mac Akmal Jahan Mohamed Abdul Cader, Senior Lecturer in Computer Science at the South Eastern University of Sri Lanka, is a highly accomplished academic and researcher, making him an exemplary candidate for the Best Researcher Award. With a career spanning over a decade, Dr. Cader has consistently demonstrated leadership in research, teaching, and academic development, particularly in the fields of artificial intelligence, computer science, and digital technologies. His research contributions, coupled with his active involvement in academic service, professional organizations, and international collaborations, solidify his standing as a leading figure in his domain.

Professional Development

Dr. Cader has participated in several professional development programs focused on effective communication, teaching and learning, and project-based learning. He has completed various certifications at QUT, enhancing his skills in pedagogy and curriculum development. His commitment to continuous improvement in education is evident in his active engagement in workshops and training sessions aimed at promoting best practices in teaching. As a Fellow of the Higher Education Academy, he champions high standards in academic instruction and student engagement.

Research Focus

Dr. Cader’s research primarily focuses on artificial intelligence, data science, and document image analysis. He explores the synthesis and application of synthetic metals, aiming to develop innovative solutions in electronics and energy storage. His work on TCNQ chemistry has significant implications for biotechnology and medicine, including the construction of electrochemical sensors and drug delivery systems. By synthesizing novel compounds, he contributes to advancements in both theoretical and practical aspects of computer science and materials research.

Awards and Honors

Senate Honours Award for High Impact Publications – SEUSL (2022 & 2023)
Queensland University of Technology Postgraduate Award (QUTPRA) (2015)
Faculty Write Up (FWU) Scholarship – QUT, Australia (2019)
Effective Communication in Teaching and Learning – QUT, Australia (2019)
Foundation of Teaching and Learning – QUT (2018)

Publication Top Notes

Locating tables in scanned documents for reconstructing and republishing | Cited by: 46 | Year: 2014
Plagiarism Detection on Electronic Text based Assignments using Vector Space Model (ICIAfS14) | Cited by: 37 | Year: 2014
AntiPlag: Plagiarism Detection on Electronic Submissions of Text Based Assignments | Cited by: 34 | Year: 2014
Plagiarism detection tools and techniques: A comprehensive survey | Cited by: 23 | Year: 2021
Fingerprint Systems: Sensors, Image Acquisition, Interoperability and Challenges | Cited by: 11 | Year: 2023
Contactless finger recognition using invariants from higher order spectra of ridge orientation profiles | Cited by: 10 | Year: 2018
Accelerating text-based plagiarism detection using GPUs | Cited by: 10 | Year: 2015
Contactless multiple finger segments based identity verification using information fusion from higher order spectral invariants | Cited by: 9 | Year: 2018

Assoc. Prof. Dr. Mahmoud Bayat | Artificial intelligence Award | Best Researcher Award

Posted on 20/12/2024 by ScienceFather

Assoc. Prof. Dr. Mahmoud Bayat | Artificial intelligence Award | Best Researcher Award

Assoc. Prof. Dr. Mahmoud Bayat, Research Institute of Forests and Rangelands, Iran

Mahmoud Bayat is an Assistant Professor at the Research Institute of Forests and Rangelands, part of the Agricultural Research, Education, and Extension Organization (AREEO) in Tehran, Iran. He earned his B.A., M.Sc., and Ph.D. degrees from the University of Tehran, specializing in forestry science. Mahmoud has collaborated with renowned researchers, including Dr. Charles P.-A. Bourque, Dr. Pete Bettinger, Dr. Eric Zenner, Dr. Aaron Weiskittel, Dr. Harold Burkhart, and Dr. Timo Pukkala. His research focuses on forest modeling and inventory, with particular interest in applying artificial intelligence and machine learning techniques in forestry. Currently, he is working on projects related to growth and yield models for uneven-aged and mixed broadleaf forests using neural networks and the monitoring and mapping of tree species richness in northern Iran’s forests through symbolic regression and artificial neural networks. Mahmoud is proficient in statistical tools such as SPSS and MATLAB, and he is eager to share his expertise and discuss potential collaborations. For more information, his profiles can be found on ResearchGate, Google Scholar, and Scopus.

Professional Profile:

SCOPUS

Mahmoud Bayat’s Suitability for the Research for Best Researcher Award

Based on the provided details, Mahmoud Bayat demonstrates a strong candidacy for the Research for Best Researcher Award due to his extensive academic and professional contributions. Below is a summary supporting his suitability

Education 🎓

Ph.D. in Forestry Science
University of Tehran, Iran
M.Sc. in Forestry Science
University of Tehran, Iran
B.A. in Forestry Science
University of Tehran, Iran

Work Experience 🏢

Assistant Professor
Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO)
Tehran, Iran
Year: [Specify Year] – Present
Research Collaborator
Worked with:
- Dr. Charles P.-A. Bourque
- Dr. Pete Bettinger
- Dr. Eric Zenner
- Dr. Aaron Weiskittel
- Dr. Harold Burkhart
- Dr. Timo Pukkala

Research Interests 🔍

Forest modeling and inventory
Application of artificial intelligence and machine learning in forestry

Current Projects 📊

Growth and Yield Models for Uneven-Aged and Mixed Broadleaf Forest
- Method: Neural Network
Monitoring, Mapping, and Modeling Variation in Tree Species Richness
- Method: Symbolic Regression and Artificial Neural Networks
- Location: Northern Iran Forests

Publication Top Notes:

Comparison of Random Forest Models, Support Vector Machine and Multivariate Linear Regression for Biodiversity Assessment in the Hyrcanian Forests

Projected biodiversity in the Hyrcanian Mountain Forest of Iran: an investigation based on two climate scenarios

Recreation Potential Assessment at Tamarix Forest Reserves: A Method Based on Multicriteria Evaluation Approach and Landscape Metrics

Comparison between graph theory connectivity indices and landscape connectivity metrics for modeling river water quality in the southern Caspian sea basin