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.

Ali Reza Keivanimehr | AI in healthcare | Best Researcher Award

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Mr.Ali Reza Keivanimehr | AI in healthcare
| Best Researcher Award

Mr.  Ali RezaKeivanimehr ,  Amirkabir University of Technology (Tehran’s Polytechnic), Iran.

Ali Reza Keivanimehr is an exceptional early-career researcher with a solid academic foundation, a promising research trajectory in machine learning applications for healthcare, and strong technical expertise. His combination of research, teaching, and technical projects highlights a well-rounded profile. His contributions, especially in the use of TinyML for cardiovascular diagnosis, are commendable and align with global health priorities.

Publication Profile

Google scholar

Education :

Master of Science in Information Technology Engineering – Internet of Things (IoT) (2022 – 2025)Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranRanked 403rd in QS World University Rankings 2024GPA: 3.53/4 (17.48/20) – 3rd highest in 2022 faculty entranceThesis: Applications of TinyML in Prediction and Diagnosis of Cardiovascular DiseasesSupervisor: Dr. Mohammad Akbari | Advisor: Dr. Abbas AhmadiBachelor of Science in Computer Engineering – Software Engineering (2018 – 2021)Imam Khomeini International University of Qazvin, Qazvin, IranProject: Designing a Software Interface for Industrial Machinery Maintenance

Experience :

Research Assistant (2022 – Present)
Data Science Lab (DSLab), Amirkabir University of Technology, Tehran, IranConducting research on TinyML and edge intelligence applications in cardiovascular disease prediction.Teaching Assistant — Machine Learning and Pattern Recognition (2024 – 2025)Amirkabir University of Technology, Tehran, IranAssisted in course instruction, project supervision, and student evaluations under Dr. Alireza Rezvanian.Teaching Assistant — Data Structure and Algorithms (2019 – 2020)
Imam Khomeini International University of Qazvin, Qazvin, IranSupported coursework delivery, assignments, and exam preparations under Morteza Mohammadi Zanjireh.

Research Focus :

Natural Language Processing (NLP)Graph Neural NetworksEdge IntelligenceExplainable Artificial Intelligence (XAI)Generative Adversarial Networks (GANs)Dr. Keivanimehr’s research centers on Tiny Machine Learning (TinyML) and edge intelligence, with a specific emphasis on their applications in cardiovascular disease monitoring. He explores the deployment of machine learning models on low-power, resource-limited devices to facilitate real-time analytics and pervasive monitoring for patients with cardiac anomalies.

Skills and Expertise:

As a research assistant, Dr. Keivanimehr has developed expertise in machine learning, classification, and supervised learning. His technical proficiency includes a focus on computational health and biomedical applications, particularly in the context of resource-constrained devices.Programming: PythonMachine Learning Frameworks: PyTorch, TensorFlowBig Data Tools: Apache SparkLanguages: TOEFL iBT (Score: 109 | Reading: 28 | Listening: 30 | Speaking: 26 | Writing: 25)

Awards:

 

48th Rank among 5000+ participants, National Entrance Exam for Master Studies in IT Engineering (2022)3rd Rank in IT Engineering Master’s cohort based on GPA (2022 – Present)Full Master’s Scholarship: Awarded for excellence in national entrance exams; covers tuition, dormitory, and partial food expenses (2022 – Present)Full Bachelor’s Scholarship: Granted for top performance in national entrance exams; included tuition, accommodation, and meal support (2018 – 2021)

 

Publication 

 

  • Keivanimehr, A., & Akbari, M. (2024). TinyML and Edge Intelligence Applications in Cardiovascular Disease: A Survey. Computers in Biology and Medicine. DOI: 10.1016/j.compbiomed.2025.109653

 

Conclusion

Ali Reza Keivanimehr is a suitable candidate for the Best Researcher Award. His strong academic record, impactful research, and consistent growth in machine learning and edge intelligence demonstrate his potential as a leading researcher in his field. With further international exposure and expanded publication efforts, he is poised to make significant contributions to both academia and industry.

 

Dongsong Zhang | Mobile health | Best Researcher Award

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 Dr.Dongsong Zhang |Mobile health|Best Researcher Award

 Dr.  Dongsong Zhang UNC Charlotte,United States.

 

Dr. Dongsong Zhang is a Professor in the Department of Business Information Systems and Operations Management at the University of North Carolina at Charlotte. He earned his Ph.D. in Management Information Systems from the University of Arizona. Dr. Zhang’s research interests include mobile and intelligent health systems, human-computer interaction, and business analytics. He has published extensively in top-tier journals and conferences, contributing significantly to the fields of information systems and health informatics.

Publication Profile

scopus

Education :

Dr. Dongsong Zhang earned his Ph.D. in Management Information Systems from The Eller College of Management at the University of Arizona in 2002. He also holds an M.S. in Natural Language Processing from the Institute of Psychology, Chinese Academy of Sciences (1995) and a B.S. in Electrical & Computer Engineering from the Branch Campus of Peking University, Beijing, China (1990).

Experience :

Dr. Zhang has extensive experience in academia and research leadership. He is currently the Interim Executive Director of the School of Data Science at the University of North Carolina at Charlotte (UNCC). Previously, he served as the Director of Research at the same school (2020-2024). Since 2018, he has been the Belk Endowed Chair Professor in Business Analytics at the Belk College of Business and a Professor (Courtesy) in Computer Science at UNCC.Prior to joining UNCC, Dr. Zhang was a Full Professor (2014-2018), Associate Professor (2007-2014), and Assistant Professor (2002-2007) in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). His early career includes research roles at the Center for the Management of Information, University of Arizona (1997-2002), and the Institute of Psychology, Chinese Academy of Sciences (1990-1996).

Research Focus :

Dr. Zhang’s research revolves around data-driven decision-making and intelligent systems, with a particular focus on:AI and Machine Learning for Business IntelligenceFake News Detection & Online Misinformation AnalysisCybersecurity, Phishing Detection, and Behavioral AnalyticsNatural Language Processing in Social Media & Public HealthHuman-Computer Interaction (HCI) & Adaptive SystemsHis projects explore how emerging technologies can be leveraged to improve cybersecurity, healthcare, and business intelligence.

 

Awards:

Dr. Zhang has received significant funding from prestigious organizations, including the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Defense (DoD), and Centers for Disease Control and Prevention (CDC). Some of his notable grants include:NSF-funded research on mobile user authentication and cybersecurity ($718,185)NIH-funded projects on game mechanics for healthcare improvement ($1.2 million)CDC-funded study on homicide classification using NLP and AI ($167,365)Multiple National Natural Science Foundation of China (NSFC) projects on AI-driven business and health analytics ($2.35 million total)In addition to research grants, Dr. Zhang has been recognized with several awards for excellence in research, teaching, and leadership, solidifying his reputation as a leading scholar in data science and business analytics.

Publication :

  • Yan, Z., Peng, F., & Zhang, D. (2025). DECEN: A Deep Learning Model Enhanced by Depressive Emotions for Depression Detection from Social Media Content. Decision Support Systems. Accepted for publication on Feb. 8, 2025.

  • Zhang, D., Shan, G., Lee, M., Zhou, L., & Fu, Z. (2025). MT-GPD: A Multimodal Deep Transfer Learning Model Enhanced by Auxiliary Mechanisms for Cross-domain Online Fake News Detection. Production and Operation Management. Accepted for publication in Jan. 2025.

  • Yu, L., Gong, W., Zhang, D., & Ding, Y. (2025). From Interaction to Prediction: A Multi-Interactive Attention-based Approach to Product Rating Prediction. INFORMS Journal on Computing. Forthcoming.

  • Zhang, D., Zhou, L., Tao, J., Zhu, T., & Gao, G. (2025). KETCH: A Knowledge-Enhanced Transformer-based Approach to Suicidal Ideation Detection from Social Media Content. Information Systems Research (ISR). Published online on May 31, 2024.

  • Peng, F., Zhang, D., & Yan, Z. (2024). Digital Phenotyping-based Depression Detection in the Presence of Comorbidity: An Uncertainty Reasoning Approach. Journal of Management Information Systems (FT 30), 41(4), 931-957.

  • Yu, L., Xing, W., & Zhang, D. (2024). Live Streaming Channel Recommendation Based on Viewers’ Interaction Behavior: A Hypergraph Approach. Decision Support Systems, 184.

  • Chen, S., Yin, S., Guo, Y., Ge, Y., Janies, D., Dulin, M., Brown, C., Robinson, P., & Zhang, D. (2023). Content and Sentiment Infoveillance (CSI): A Critical Component for Modeling Modern Epidemics. Frontiers in Public Health, 11, 1111661. https://doi.org/10.3389/fpubh.2023.1111661. PMID: 3700.

 

Conclusion 

Dr. Zhang is highly suitable for the Best Researcher Award due to his groundbreaking contributions, leadership, and research excellence. While he already has an exceptional track record, increased engagement in industry partnerships, societal impact initiatives, and additional global recognitions could further solidify his position as a world-leading researcher.

 

 

 

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.