Ahmed Ismail | Computer technology | Best Researcher Award

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Mr. Ahmed Ismail | Computer technology | Best Researcher Award

Dr.med.student ,Universität Bonn, Germany

Dr. Ismail is a seasoned researcher whose work in food chemistry, safety, and nutrition addresses critical challenges relevant to health and industry. His consistent publication record, student mentorship, and leadership in funded projects all make him a compelling candidate for the Best Researcher Award.

Publication Profile

Education:

Ismail holds a Master of Science in Pharmacognosy from the Faculty of Pharmacy at Sana’a University, Yemen, which he completed in 2013. He previously earned a Bachelor of Pharmacy (BPharm) from the same university in 2008. His postgraduate studies have equipped him with a deep understanding of medicinal plants, natural products, and their pharmacological properties.

Professional Experience:

Ismail currently serves as a Lecturer in the Department of Pharmacognosy at the Faculty of Pharmacy, Sana’a University. His academic role includes delivering lectures, supervising practical sessions, and guiding student research projects. Additionally, he contributes to curriculum development and department-level academic activities. Over the years, Ismail has built a reputation for his dedication to teaching and scientific inquiry in the field of natural product pharmacology.

Skills:

Ismail possesses strong expertise in pharmacognosy and phytochemistry, with practical skills in the extraction, isolation, and analysis of bioactive compounds from medicinal plants. He is experienced in the application of chromatographic and spectroscopic techniques such as TLC, HPLC, and UV-Vis spectrometry. Furthermore, he demonstrates proficiency in scientific research, manuscript preparation, and academic supervision.

Awards and Scholarships:

Ismail was awarded the MEXT Scholarship by the Japanese Government in 2017, enabling him to pursue research in Japan. He also received the President’s Award for Academic Excellence during his undergraduate studies at Sana’a University. These accolades reflect his commitment to academic excellence and international scientific collaboration.

Research Focus:

Ismail’s research centers on the pharmacological and phytochemical investigation of medicinal plants, particularly those used in traditional Yemeni medicine. He is interested in identifying bioactive compounds with potential therapeutic effects, especially for antimicrobial and antioxidant applications. His work aims to bridge the gap between traditional knowledge and modern pharmaceutical science.

Publication : 

Dr. Ismail is highly suitable for the Best Researcher Award, especially in domains connected to Food Technology, Safety, and Public Health. His scholarly contributions, research leadership, and professional involvement reflect a deep dedication to science and make him a strong contender. Enhancing international collaboration and showcasing innovation outcomes would further elevate his profile.

 

Sajad Rezvani | Computer vision | Excellence in Research

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Mr Sajad Rezvani | Computer vision | Excellence in Research

Shahrood University of Technology , Iran

Sadjad Rezvani is a highly qualified candidate for the Research for Excellence in Research award. His impressive academic achievements, impactful research contributions, technical expertise, and leadership in mentoring make him a strong contender. His work in masked face recognition, medical image analysis, and image segmentation reflects both the depth and relevance of his research in today’s rapidly evolving tech landscape.

Publication Profile
scopus

Education :

Sadjad Rezvani holds a Master of Science in Computer Engineering with a specialization in Artificial Intelligence from Shahrood University of Technology, Iran. He completed his master’s degree between September 2020 and September 2022, graduating with a GPA of 4/4 (18.59/20). His thesis was titled “Masked Face Recognition Using Deep Learning,” under the guidance of Professor Mansoor Fateh. Prior to this, Sadjad earned his Bachelor of Science in Computer Engineering, specializing in Software Engineering, from Shahrood University of Technology, completing his degree between September 2015 and September 2019 with a GPA of 3.53/4 (16.92/20). His undergraduate thesis was titled “Profiling Web Applications to Improve Intrusion Detection,” supervised by Professor Mohsen Rezvani.

Professional Experience:

Sadjad has practical experience as a Computer Vision Software Engineer in several industries. He worked at Hookan Salt Factory in Shiraz, Iran, from November 2020 to September 2021, where he contributed to the development of a Salt Crack Sorting Machine. In this role, he employed advanced image processing techniques to detect salt impurities in real-time, utilizing tools such as OpenCV, Python, C#, and C++. Additionally, he worked at Shahaab, CO from June 2019 to December 2023 on a Plate Recognition Software project, where he contributed to a system that recognized license plates using CCTV camera data. His work involved maintaining and improving the software using C#, SQL, and other related technologies.

Research Skills:

Sadjad is highly skilled in programming languages such as Python, C++, and C#, and has a strong background in Machine Learning frameworks including PyTorch, TensorFlow, and Scikit-Learn. He is proficient in Computer Vision tools like OpenCV and has experience with databases such as Microsoft SQL Server and MySQL. His technical expertise also extends to advanced image processing, AI for medical diagnosis, and deep learning-based solutions for real-world applications.

Research Focus :

Sadjad’s research interests include Machine Learning (ML), Deep Learning (DL), Generative AI (GenAI), Medical Image Analysis, Limited Data Solutions, and Domain Adaptation. He has contributed to several journal publications, such as the development of ABANet: Attention Boundary-Aware Network for Image Segmentation (2024) and a paper on Single Image Denoising via a New Lightweight Learning-Based Model (2024), among others. His academic research also includes the application of deep learning models for lung CT image segmentation and innovations in masked face recognition using deep learning.

 

Awards :

Sadjad has received recognition for his achievements, including being a member of Iran’s National Elites Foundation in 2023 and being the third-ranked student in his Master of Science program. His certifications include AI for Medical Diagnosis from DeepLearning.AI (Coursera, 2023), Python Project for Data Science from IBM (Coursera, 2022), and specialization courses in Generative Adversarial Networks (GANs) and Machine Learning from Stanford University.

Honours and Awards

  • Member of Iran’s National Elites Foundation, 2023

  • Third-ranked student in the Master of Science in Computer Science program, 2022

 

Publication : 

 

    • Rezvani, S., Fateh, M., & Khosravi, H. (2024). ABANet: Attention Boundary-Aware Network for Image Segmentation. Expert Systems, e13625. [Published May 2024]

    • Rezvani, S., Soleymani Siahkar, F., Rezvani, Y., Alavi Gharahbagh, A., & Abolghasemi, V. (2024). Single Image Denoising via a New Lightweight Learning-Based Model. IEEE Access, August 2024.

    • Rezvani, S., Fateh, M., Fateh, A., & Jalali, Y. (2024). FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation. Biomedical Signal Processing and Control, Revised Sep 2024.

conclusion:

  • Sadjad’s overall profile is well-rounded with strengths across research, academia, technical skills, and professional experience.

  • Continued focus on expanding publication reach, collaboration, and public speaking could further elevate his visibility and impact in the research community.

  • With his dedication and achievements, Sadjad is well-positioned for recognition in research excellence.

In conclusion, Sadjad is a strong candidate for the award, and with a few adjustments in outreach and collaboration, he could continue to make significant strides in the research world.

 

Francisco Javier Lima Florido | Artificial Intelligence | Best Researcher Award

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Mr Francisco Javier Lima Florido | Artificial Intelligence | Best Researcher Award

Researcher in training , University of Malaga , Spain

Francisco Javier Lima Florido is an accomplished researcher whose work in Machine Learning, Deep Learning, and Natural Language Processing has significant practical and academic merit. His focus on multilingual dialogue systems, health applications, and automatic interpretation solutions speaks to his expertise and potential to impact society through technology. As a PhD student, he is still developing his academic career but has already made noteworthy contributions to the field through participation in significant projects.

Publication Profile
scopus

Education :

Francisco Javier Lima Florido holds a Bachelor’s degree in Software Engineering from the University of Málaga (2016). He also earned a Master’s degree in Software Engineering and Artificial Intelligence from the same institution in 2019. Currently, Francisco is pursuing a PhD in the Translation and Interpreting Department at the University of Málaga, where his research is primarily focused on the intersection of technology and language.

Experience:

Francisco has actively participated in various research projects throughout his academic career. Notably, he was involved in the VIP: Integrated Voice-Text System for Interpreters project. This project explored the integration of voice and text systems for interpreters. Presently, he is contributing to cutting-edge projects like the Neural-based multilingual dialogue systems for the development of health apps (focusing on triage in Spanish, English, and Arabic) and the MI4ALL – Automatic Interpretation for All Using a Deep Learning-based API transfer project. These initiatives demonstrate his extensive experience in developing machine learning models for natural language processing (NLP).

Research Focus:

His primary research interests lie in the application of Machine Learning and Deep Learning techniques to Natural Language Processing (NLP). He is particularly focused on the development of multilingual dialogue systems and automatic interpretation technologies. His work aims to enhance the functionality and accessibility of tools for interpreters and healthcare applications, with a special interest in bridging communication gaps in multilingual settings.

Skills:

Francisco is highly skilled in several areas within Software Engineering and Artificial Intelligence, with a strong emphasis on Machine Learning and Deep Learning. His technical expertise spans:

    • Natural Language Processing (NLP)
    • Multilingual Dialogue Systems
    • Deep Learning Algorithms
    • Machine Learning Model Development
    • Speech-to-Text Technologies
    • Python Programming and related frameworks (e.g., TensorFlow, PyTorch)

 

Publication :

Francisco Javier Lima Florido has contributed to several research projects and publications in the fields of Machine Learning, Deep Learning, and Natural Language Processing. Notably:​

  1. “Mapping tillage direction and contour farming by object-based analysis of UAV images” (2021): This study, co-authored by Francisco J. Lima-Cueto, Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, José Dorado, and José M. Peña, was published in Computers and Electronics in Agriculture. ​

  2. “Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain” (2019): Authored by Francisco J. Lima-Cueto, Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, and Federico B. Galacho-Jiménez, this paper appeared in Remote Sensing. ​

Additionally, Francisco Javier Lima Florido has been involved in research projects such as “VIP: Integrated Voice-Text System for Interpreters” and is currently participating in “Neural-based multilingual dialogue systems for the development of health apps: triage (Spanish – English/Arabic)” and the transfer project “MI4ALL – Automatic Interpretation For All Using a Deep Learning-based API”.

conclusion:

Francisco is highly deserving of consideration for the “Best Researcher Award.” His expertise in cutting-edge AI technologies, especially in the context of language translation and interpretation, holds immense potential for positive social impact. While there are areas for improvement, such as enhancing his publication record and broadening his collaborative network, his current research trajectory shows great promise. His ongoing contributions to AI research and application indicate that he is on a path to becoming a leading figure in the field.

Jaehwan Jeong | Generative AI models | Best Researcher Award

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Mr. Jaehwan Jeong | Generative AI models | Best Researcher Award

Ph.D Student,Korea University,South Korea

Jaehwan Jeong is an emerging researcher in AI and computer vision, with a strong academic background and collaborations with top institutions. His work in deepfake defense and generative models positions him well for awards in AI safety and multi-modal learning. However, securing additional accepted publications and leading independent research could further bolster his case for the Best Researcher Award.

Publication Profile
Google Scholar

Education :

Jaehwan Jeong is currently pursuing a Ph.D. in Artificial Intelligence at Korea University, Seoul, South Korea (2024–2029, expected). He completed his Bachelor of Engineering (B.E.) in Electrical & Electronic Engineering from Chung-Ang University, Seoul, in 2021. His academic journey has been focused on artificial intelligence, deep learning, and computer vision.

Experience:

Research Focus:

Skills:

Jaehwan possesses strong expertise in:,Programming: Python, Shell Scripting, Git, LaTeX,Deep Learning Frameworks: PyTorch, PyTorch Lightning, TensorFlow,AI & ML Libraries: Hugging Face, Scikit-Learn

Publication :

  • MTVG: Multi-text Video Generation with Text-to-Video Models

    • Authors: Gyeongrok Oh, Jaehwan Jeong, Sieun Kim, Wonmin Byeon, Jinkyu Kim, Sungwoong Kim, Hyeokmin Kwon, Sangpil Kim
    • Publication: arXiv preprint arXiv:2312.04086
    • Year: 2023
    • Citation: Oh, G., Jeong, J., Kim, S., Byeon, W., Kim, J., Kim, S., Kwon, H., & Kim, S. (2023). MTVG: Multi-text Video Generation with Text-to-Video Models. arXiv preprint arXiv:2312.04086.
      kuai-lab.github.io

  • MEVG: Multi-event Video Generation with Text-to-Video Models

    • Authors: Gyeongrok Oh, Jaehwan Jeong, Sieun Kim, Wonmin Byeon, Jinkyu Kim, Sungwoong Kim, Sangpil Kim
    • Publication: European Conference on Computer Vision (ECCV), pages 401–418
    • Year: 2025
    • Citation: Oh, G., Jeong, J., Kim, S., Byeon, W., Kim, J., Kim, S., & Kim, S. (2025). MEVG: Multi-event Video Generation with Text-to-Video Models. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024 (pp. 401–418). Springer.
      pure.korea.ac.kr

  • FaceShield: Defending Facial Image against Deepfake Threats

    • Authors: Jaehwan Jeong, Seungmin In, Sieun Kim, Hyojin Shin, Jaeho Jeong, Seunghyun Yoon, Jaewon Chung, Sungwoong Kim
    • Publication: arXiv preprint arXiv:2412.09921
    • Year: 2024
    • Citation: Jeong, J., In, S., Kim, S., Shin, H., Jeong, J., Yoon, S., Chung, J., & Kim, S. (2024). FaceShield: Defending Facial Image against Deepfake Threats. arXiv preprint arXiv:2412.09921.
Conclusion:

Jeong is a strong candidate for the award but would benefit from more accepted publications and demonstrated leadership in independent research. His ongoing Ph.D. work and collaborations with Samsung, NVIDIA, and Google make him a promising researcher with significant potential.

Saba Inam | machine learning | Women Researcher Award

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Dr.Saba Inam |machine learning| Women Researcher Award

Dr Saba InamFatima Jinnah women university, The Mall, Rawalpindi, Pakistan.

Dr. Saba Inam is a lecturer in the Department of Mathematical Sciences at Fatima Jinnah Women University in Rawalpindi, Pakistan. She earned her PhD in Algebraic Cryptography from the Capital University of Science and Technology, completing her studies between February 2014 and January 2019. Her research interests include Algebraic Number Theory, Algebraic Cryptography, Applied Cryptography, image encryption, Cloud Computing, Machine Learning, and Deep Learning. Dr. Inam has contributed to over 20 publications, accumulating 247 citations, and her work has garnered more than 3,367 reads. Notably, she co-authored the article “An efficient image encryption algorithm using 3D-cyclic Chebyshev map and elliptic curve,” published in November 2024.

Publication Profile

Google Scholar

Orcid

Education :

Dr. Saba Inam holds a PhD in Mathematics from Capital University of Science and Technology (CUST), Islamabad (2019). She completed her MS in Mathematics from COMSATS Institute of Information Technology, Islamabad, in 2007 with a CGPA of 3.6/4, achieving 1st Division. She earned her M.Sc. in Mathematics from Quaid-i-Azam University, Islamabad (2005), and her B.Sc. in Mathematics (Maths A, Maths B, Stats) from the University of the Punjab (2003), both with 1st Division.

Experience :

Dr. Inam has extensive academic and research experience. Since September 2007, she has been serving as a Lecturer in Mathematics at Fatima Jinnah Women University, Rawalpindi. She also held the position of Incharge, Department of Mathematical Sciences from August 2016 to January 2018. Before that, she worked as a Research Associate at COMSATS Institute of Information Technology, Islamabad, from March to August 2007.

Research Focus :

Dr. Inam’s research interests span across multiple domains, including:

Cryptography & Security: Algebraic Cryptography, Cryptology, CryptanalysisAI & Data Security: Image Encryption, Blockchain, IoT, Deep Learning, Machine LearningMathematical Sciences: Fluid Mechanics, Geometric Function Theory.

 

Awards:

Dr. Inam’s academic excellence has been recognized through various awards and honors:

Scholarship – Capital University of Science and Technology (CUST), Islamabad (2013-2018)Dean’s Roll of Honor – Received twice during PhD courseworkDiploma in Academic Excellence in Discrete Mathematics – Abdul Salam School of Mathematical Sciences, GC University, Lahore (2012)Scholarship – COMSATS Institute of Information Technology, Islamabad (2005-2007)

Skills:

Dr. Inam possesses expertise in:Programming & Computational Tools: Matlab, Python, Mathematica, APCoCoA, Scientific Workplace, LaTeXOffice & Documentation: Proficient in Microsoft Office Suite,Dr. Saba Inam continues to contribute significantly to the fields of cryptography, image encryption, and mathematical security frameworks, with a strong focus on deep learning and blockchain applications.

Publication :

 

Three dimensional boundary layer flow of a viscoelastic nanofluid with Soret and Dufour effects

Numerical simulation of Darcy–Forchheimer 3D unsteady nanofluid flow comprising carbon nanotubes with Cattaneo–Christov heat flux and velocity and thermal slip conditions

An effective color image encryption based on Henon map, tent chaotic map, and orthogonal matrices

A new ElGamal-like cryptosystem based on matrices over groupring

Analytic study of a novel color image encryption method based on the chaos system and color codes

Blockchain based medical image encryption using Arnold’s cat map in a cloud environment

A numerical study of rotating Bödewadt flow of micropolar fluid over porous disk

A Novel Public Key Cryptosystem and Digital Signatures

A new method of image encryption using advanced encryption Standard (AES) for network security

Research on color image encryption approach based on chaotic Duffing map

Conclusion

Dr. Inam’s strengths, including her academic excellence, research impact, and leadership in academia, position her as a strong candidate for the “Research for Women Researcher Award.” While there are areas to enhance collaboration and public outreach, her work is already shaping critical developments in data security and encryption. Recognizing her would not only celebrate her achievements but also inspire more women to pursue careers in research and technology, contributing significantly to both academic and societal progress.