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.

Mr.Nisar Hussain |Artificial Intelligence and Machine Learning|Best Researcher Award

Mr. Nisar Hussain Instituto Politechnico Nacional, Mexico City, Mexico

Nisar Hussain is a researcher affiliated with the Instituto Politécnico Nacional (IPN) in Mexico City, Mexico. He is currently enrolled in the Doctorate in Computer Science program at IPN’s Centro de Investigación en Computación (CIC), focusing his research on offensive language detection and sentiment analysis in code-mixed text on social media.Throughout his academic career, Hussain has contributed to various studies in the field of Natural Language Processing (NLP). Notably, he co-authored the paper titled “ORUD-Detect: A Comprehensive Approach to Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning–Deep Learning Models with Embedding Techniques,” published in the journal Information in February 2025.In addition to his work on offensive language detection, Hussain has explored other areas of NLP. He co-authored a study on guilt detection in text, which was published in Scientific Reports in July 2023.

Publication Profile

Google scholar

orcid

Education :

Ph.D. in Computer Science (2022-2025, Ongoing)
Instituto Politécnico Nacional, MéxicoMaster’s in Computer Science (2014-2017)
University of Agriculture, Faisalabad, PakistanBachelor of Science in Computer Science (BSCS) (2010-2014)
COMSATS University Islamabad, Sahiwal Campus

Experience :

With 4+ years of experience in developing and deploying ML and NLP systems, I have actively contributed to multiple projects, applying NLP techniques for real-world problem-solving. I have worked with large, complex datasets, implementing hybrid ML-DL approaches for automated text processing, sentiment analysis, and multilingual content understanding. My research collaborations span multiple institutions, focusing on AI-driven solutions for text analysis and detection tasks.

Research Focus :

I specialize in Natural Language Processing (NLP) and Machine Learning, with a particular emphasis on Offensive Language Detection and Sentiment Analysis of Code-Mixed Data. My research explores multilingual and low-resource language models, leveraging and fine-tuning mBERT, XLM-R, IndicBERT, and Google’s BERT-based models. I am particularly interested in hate speech detection, sentiment analysis, language identification, and emotion analysis in complex linguistic environments. My work integrates deep learning techniques, transformers, and hybrid ML-DL models to improve text processing and understanding in diverse multilingual contexts.

Awards:

Published multiple high-impact research papers in leading AI and NLP conferences/journalsActive participant in international AI competitions and workshopsRecognized for contributions to multilingual and low-resource NLP advancements

Publication :

Shaheen, M., Awan, S. M., Hussain, N., & Gondal, Z. A. (2019). Sentiment analysis on mobile phone reviews using supervised learning techniques. IJMECS, 11(7), 32.

Mehak, G., Qasim, A., Meque, A. G. M., Hussain, N., Sidorov, G., & Gelbukh, A. (2025, January). TechExperts (IPN) at GenAI Detection Task 1: Detecting AI-Generated Text in English and Multilingual Contexts. In Proceedings of the 1st Workshop on GenAI Content Detection (GenAIDetect) (pp. 161-165).

Hussain, N., Qasim, A., Mehak, G., Kolesnikova, O., Gelbukh, A., & Sidorov, G. (2025). Hybrid Machine Learning and Deep Learning Approaches for Insult Detection in Roman Urdu Text. AI, 6(2), 33. https://doi.org/10.3390/ai6020033

Qasim, A., Mehak, G., Hussain, N., Gelbukh, A., & Sidorov, G. (2025). Detection of Depression Severity in Social Media Text Using Transformer-Based Models. Information, 16(2), 114. https://doi.org/10.3390/info16020114

Manzoor, M. I., Shaheen, M., Khalid, H., Anum, A., Hussain, N., & Faheem, M. R. (2018). Requirement Elicitation Methods for Cloud Providers in IT Industry. IJMECS, 10(10).

Hussain, N., & Anees, T. (2018). Development of a novel approach to search resources in IoT. International Journal of Advanced Computer Science and Applications, 9(9).

Faheem, M. R., Iftikhar, A., & Hussain, N. (2022). Automated Diagnosing of Eye Disease in Real Time. Journal of Computing & Biomedical Informatics, 3(1), 282-288.

Shaheen, M., Anees, T., Hussain, N., & Obaid, I. (2019, April). A Research on SOA in the IT Industry of Pakistan. In Proceedings of the 2019 ICCTA (pp. 149-154).

Meque, A. G. M., Hussain, N., Sidorov, G., & Gelbukh, A. (2023). Guilt Detection in Text: A Step Towards Understanding Complex Emotions. arXiv preprint arXiv:2303.03510.

Tash, M. S., Ahani, Z., Tonja, A., Gemeda, M., Hussain, N., & Kolesnikova, O. (2022, December). Word Level Language Identification in Code-mixed Kannada-English Texts using Traditional Machine Learning Algorithms. In Proceedings of the (ICON) (pp. 25-28).

Conclusion

Given their strong publication record, hands-on experience with AI models, and focus on low-resource NLP, the candidate is highly competitive for the Best Researcher Award. Strengthening industry collaborations, increasing research impact, and securing grants will further enhance their research profile.