Ms. Ifza Shad | Computer Vision | Research Excellence Award

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Ms. Ifza Shad | Computer Vision | Research Excellence Award

University of Central Punjab | Pakistan

Ms. Ifza Shad is a computer vision and artificial intelligence researcher whose work focuses on real-time object detection, medical image analysis, deep learning optimization, and multimodal perception models for complex environments. Her research integrates advanced machine learning architectures, including YOLO-based detectors, attention-driven fusion networks, and lightweight deep learning frameworks designed for resource-efficient deployment in dynamic real-world scenarios. She has contributed to cutting-edge studies in aquatic and surface litter detection, brain tumor diagnosis, protective workwear recognition, and driver-behavior monitoring systems, demonstrating a strong emphasis on safety, healthcare, and environmental sustainability. Her interdisciplinary approach merges computer vision, robotics, and large-scale data processing, allowing her to design algorithms that address challenges in automation, public health, and smart systems. She has authored impactful publications in reputable international journals indexed in Scopus and Web of Science, with her research widely cited and accessible on Google Scholar. Her scholarly record includes peer-reviewed articles, collaborative projects with international researchers, and contributions to academic seminars and conferences. She continues to advance innovative detection models and AI-driven solutions, aiming to enhance real-time decision support systems through robust, interpretable, and computationally efficient algorithms. Her research output reflects a growing citation count, supported by Scopus metrics, Google Scholar indices, and document-level analytics, emphasizing her active role in the global scientific community and her contribution to emerging intelligent systems.

Profile

ORCID

Featured Publications

Shad, I., Zhang, Z., Asim, M., Al-Habib, M., Chelloug, S. A., & Abd El-Latif, A. (2025). Deep learning-based image processing framework for efficient surface litter detection in computer vision applications. Journal of Radiation Research and Applied Sciences, 18(2), 101534.

Shad, I., Bilal, O., & Hekmat, A. (2025). Attention-driven sequential feature fusion framework for effective brain tumor diagnosis. Significances of Bioengineering & Biosciences, 7(3).

Hekmat, A., Zhang, Z., Khan, S. U. R., Shad, I., & Bilal, O. (2024). An attention-fused architecture for brain tumor diagnosis. Biomedical Signal Processing and Control, 101, 107221.

Assoc. Prof. Dr. Ammar Oad | Computer Vision | Research Excellence Award

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Assoc. Prof. Dr. Ammar Oad | Computer Vision | Research Excellence Award

Professor | Shaoyang University | China

Assoc. Prof. Dr. Ammar Oad is an accomplished researcher in Artificial Intelligence with strong expertise in deep learning, computer vision, cybersecurity, and intelligent data-driven systems. His research focuses on designing advanced algorithms for image analysis, object detection, multimodal learning, cross-modal retrieval, and secure AI frameworks capable of addressing modern challenges in threat detection and autonomous systems. Dr. Oad’s scientific contributions span AI-powered fake news detection, plant disease identification using explainable AI, blockchain-enabled cybersecurity mechanisms, sustainable smart grid prediction models, and intelligent pattern recognition. His research impact is reflected in Scopus metrics of 382 citations across 374 documents with an h-index of 9, and Google Scholar metrics of 573 citations, h-index 10, and i10-index 12, demonstrating strong visibility and influence within the scientific community. His work regularly appears in reputable journals such as IEEE Access, Optik, Electronics (MDPI), and leading materials science journals through interdisciplinary collaborations. Dr. Oad also contributes to the academic community as an editorial board member and scientific reviewer for several high-impact journals. His research interests include deep neural architectures, Gaussian mixture models, ensemble learning, blockchain security frameworks, and energy-efficient AI systems for smart cities. By integrating machine learning with cybersecurity principles, he aims to develop intelligent, robust, and transparent AI solutions capable of safeguarding digital infrastructures while advancing the state of automated recognition and decision-making technologies. His growing body of research reflects innovation, rigor, and a commitment to addressing real-world AI challenges.

Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Oad, A., Farooq, H., Zafar, A., Akram, B. A., Zhou, R., & Dong, F. (2024). Fake news classification methodology with enhanced BERT. IEEE Access, 12, 164491–164502.

Oad, A., Abbas, S. S., Zafar, A., Akram, B. A., Dong, F., Talpur, M. S. H., & Uddin, M. (2024). Plant leaf disease detection using ensemble learning and explainable AI. IEEE Access, 12, 156038–156049.

Oad, A., Ahmad, H. G., Talpur, M. S. H., Zhao, C., & Pervez, A. (2023). Green smart grid predictive analysis to integrate sustainable energy of emerging V2G in smart city technologies. Optik, 272, 170146.

Oad, A., Razaque, A., Tolemyssov, A., Alotaibi, M., Alotaibi, B., & Zhao, C. (2021). Blockchain-enabled transaction scanning method for money laundering detection. Electronics, 10(15), 1766.

Li, Y., Liu, W., Pang, X., Oad, A., Liang, D., Zhang, X., Tang, B., Fang, Z., Shi, Z., & Chen, J. (2024). Microwave dielectric properties, Raman spectra and sintering behavior of low loss La7Nb3W4O30 ceramics with rhombohedral structure. Ceramics International.

Prof. Joongrock Kim | Computer Vision | Best Researcher Award

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Prof. Joongrock Kim | Computer Vision | Best Researcher Award

Associate Professor | Changwon National University | South Korea

Prof. Joongrock Kim is an accomplished researcher and Associate Professor in Artificial Intelligence Convergence Engineering at Changwon National University, Republic of Korea. His expertise spans computer vision, 3D scene understanding, deep learning-based perception, and intelligent systems for automotive and consumer applications. Over his distinguished career, he has contributed significantly to the development of advanced AI technologies, including driver monitoring systems, 3D reconstruction, food recognition, and smart V2X perception systems. His research focuses on integrating multimodal sensing, neural rendering, and adaptive feature extraction for robust real-world perception, bridging academia and industry to advance AI deployment in smart vehicles and appliances. Dr. Kim’s prolific output includes numerous high-impact publications and international patents on AI-based sensing and perception systems. According to Scopus, he has achieved 212 citations across 207 documents with an h-index of 7, while his Google Scholar profile reflects broader academic engagement and influence. His work continues to drive innovation in perception AI, human–machine interaction, and computational imaging, establishing him as a leading figure in applied artificial intelligence and computer vision research.

Profile

Scopus

Featured Publications

Park, M., Do, M., Shin, Y. J., Yoo, J., Hong, J., Kim, J., & Lee, C. (2024). H2O-SDF: Two-phase learning for 3D indoor reconstruction using object surface fields. International Conference on Learning Representations (ICLR).

Kim, J., Yu, S., Kim, D., Toh, K.-A., & Lee, S. (2017). An adaptive local binary pattern for 3D hand tracking. Pattern Recognition.

Kim, J., Yoon, C. (2016). Three-dimensional head tracking using adaptive local binary pattern in depth images. International Journal of Fuzzy Logic and Intelligent Systems.

Kim, K., Kim, J., Choi, J., Kim, J., & Lee, S. (2015). Depth camera-based 3D hand gesture controls with immersive tactile feedback for natural mid-air gesture interactions. Sensors.

Kim, J., Yu, S., & Lee, S. (2014). Random-profiles-based 3D face recognition system. Sensors.

Mr. Sachin Sravan Kumar Komati | Deep Learning | Best Researcher Award

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Mr. Sachin Sravan Kumar Komati | Deep Learning | Best Researcher Award

AI Engineer | Florida International University | United States

Sachin Sravan Kumar Komati is an accomplished researcher in Artificial Intelligence and Machine Learning, specializing in biomedical applications, particularly in gastrointestinal disease diagnosis, cancer prognosis, and postoperative complication prediction. His research integrates deep learning, computer vision, and multimodal AI frameworks to develop intelligent healthcare solutions. He has contributed significantly to the fields of predictive analytics, medical imaging, and surgical AI, creating advanced models using LSTM, Vision Transformers, and Autoencoders for enhanced diagnostic precision. His works explore AI-driven insights in clinical and imaging datasets, focusing on improving real-time disease detection and patient-specific treatment strategies. Sachin’s scholarly contributions include numerous peer-reviewed publications in reputed international journals such as PLOS One, Gastroenterology, Gastrointestinal Endoscopy, Critical Care Medicine, and the Journal of Clinical Oncology. His research has earned global recognition through multiple conference acceptances, including at ACG, AASLD, and UEG Week. According to Google Scholar, he has received 2 citations, with an h-index of 1 and an i10-index of 0, reflecting his emerging influence in AI-driven healthcare research. His Scopus metrics also indicate growing visibility and scholarly impact. Sachin’s research continues to advance the integration of artificial intelligence into clinical decision-making and medical imaging, aiming to bridge the gap between AI innovation and patient-centered healthcare.

Profile

Google Scholar | ORCID

Featured Publications

Boppana, S. H., Tyagi, D., Komati, S. S. K., Boppana, S. L., Raj, R., & Mintz, C. D. (2025). AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients. PLOS One, 20(6), e0322032.

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., Aakash, F., & Dang, A. K. (2025). Enhancing gastrointestinal bleeding detection in wireless capsule endoscopy using convolutional autoencoders. American College of Gastroenterology, 120(10S2).

Boppana, S. H., Chitturi, R. H., Komati, S. S. K., Raj, R., & Mintz, C. D. (2025). DiabCompSepsAI: Integrated AI model for early detection and prediction of postoperative complications in diabetic patients using a Random Forest Classifier. Journal of Clinical Medicine, 14(20), 7173.

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., & Mintz, C. D. (2025). Predictive modeling of GI disease: GastroEndo-Seq for progression and outcome forecasting. Gastroenterology, 120(10S2).

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., & Mintz, C. D. (2025). Vision Transformer-based framework for risk stratification and prognostic assessment in gastrointestinal lesion management. Gastrointestinal Endoscopy, 120(10S2).

Thittaporn Ganokratanaa | Computer Vision | Best Researcher Award

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Assist. Prof. Dr. Thittaporn Ganokratanaa | Computer Vision | Best Researcher Award

Lecturer at King Mongkut’s University of Technology Thonburi, Thailand

Dr. Thittaporn Ganokratanaa is an Assistant Professor in the Applied Computer Science Programme at King Mongkut’s University of Technology Thonburi. She is a dynamic academic leader involved in national and international committees including IEEE and AIAT. She actively advises innovation projects and engages in AI policy shaping in Thailand. With a strong academic and research background, she contributes significantly to the fields of artificial intelligence and multimedia signal processing. Dr. Thittaporn is widely recognized for her innovative spirit, mentorship, and leadership in applied research and education.

Publication Profile🌏📚

Academic Background🎓

Dr. Thittaporn holds a Ph.D. in Electrical Engineering with a focus on Multimedia and Signal Processing from Chulalongkorn University, with research collaboration at the University of Trento, Italy. She earned her M.Eng. from Chulalongkorn University with a GPA of 3.92 and her B.Sc. in Media Technology with first-class honors and a gold medal from KMUTT. Her academic journey is marked by multiple prestigious scholarships and fellowships, reflecting her academic excellence and commitment to research in AI, signal processing, and biomedical technology.

Professional Experience📊

Dr. Thittaporn currently serves as an Assistant Professor at KMUTT and holds several key leadership roles including Secretary of the IEEE Thailand Section and committee positions in IEEE MGA, CQC, and AIAT. She has contributed to national AI advisory committees and has served as advisor to several award-winning student innovation projects. Her career is defined by interdisciplinary collaboration, global engagement, and dedication to advancing computer science and AI education. She actively participates in conferences, policy development, and technical review roles in the academic and governmental sectors.

Awards and Honors🏆🥇

Dr. Thittaporn has received numerous prestigious awards, including the Grand Prize and Gold Medal at JDIE2024, multiple National Research Council of Thailand innovation awards, and Best Presentation at CSoNet 2024. She has been awarded both nationally and internationally for her innovative projects such as robotic prosthetics and AI-driven healthcare solutions. Her mentorship has led to student accolades at events like NSC and CommTECH. Recognized by organizations like UNOOSA and NUS, her work continues to drive excellence in AI research and technological innovation

Research Focus🔬

Dr. Thittaporn’s research interests span artificial intelligence, video anomaly detection, computer vision, human-computer interaction, multimedia signal processing, and the Internet of Things. She focuses on applying machine learning to solve real-world problems in healthcare, education, and smart technologies. Her projects include intelligent assistive devices, AI-powered learning platforms, and robotic systems. She integrates innovation with societal impact, aiming to bridge research and practical applications. Her interdisciplinary approach and global collaborations support her goal of creating technology that is ethical, inclusive, and transformative.

Publication Top Notes📊

Unsupervised anomaly detection and localization based on deep spatiotemporal translation network
citation: 123
year: 2020

Video anomaly detection using deep residual-spatiotemporal translation network
citation: 39
year: 2022

Iot system design for agro-tourism
citation: 33
year: 2021

Development of a process to enhance the reimbursement efficiency with OCR and ontology for financial documents
citation: 32
year: 2022

Voice-activated assistance for the elderly: Integrating speech recognition and IoT
citation: 20
year: 2024

Sorting red and green chilies by digital image processing
citation: 19
year: 2023

Smart agricultural greenhouses for earthworm farming
citation: 19
year: 2023

Pillow for detecting snoring with embedded techniques for elderly people with snoring problems
citation: 16
year: 2023

Real-Time Credit Card Fraud Detection Surveillance System
citation: 16
year: 2023

Conclusion🌏

Dr. Thittaporn Ganokratanaa is an outstanding candidate for the Best Researcher Award, with a strong track record in artificial intelligence, computer vision, multimedia signal processing, and human-computer interaction. Her academic excellence—evident from her Ph.D. in Electrical Engineering with international collaboration and multiple scholarships—pairs seamlessly with her innovation-driven research, reflected in numerous national and international awards, including from NRCT and JDIE. She actively contributes to impactful real-world applications, such as AI-assisted healthcare technologies and smart systems. Her leadership roles in IEEE Thailand, the AI Association of Thailand, and advisory committees for national AI policy underscore her influence in both academia and policy. Additionally, her mentorship of award-winning student projects highlights her dedication to shaping future researchers. Overall, Dr. Thittaporn exemplifies the qualities of a top-tier researcher with global impact, national relevance, and visionary leadership.

 

 

Mr. Kostas Ordoumpozanis | Computer Vision | Best Researcher Award

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Mr. Kostas Ordoumpozanis | Computer Vision | Best Researcher Award

Phd Cand. Department of Cultural Technology and Communication, University of the Aegean Greece, Greece

Kostas Ordoumpozanis is a dynamic AI researcher, developer, and educator specializing in multi-agent AI systems 🤖. Currently a PhD candidate at the University of the Aegean, Greece, his expertise spans AI-driven automation, large language models (LLMs), retrieval-augmented generation (RAG), and AI-human collaboration. With a background in mechanical engineering and over a decade of experience in full-stack development, AR, and creative technology, he blends technical proficiency with artistic innovation. Kostas is also an entrepreneur, recognized for his startup ventures in AI and augmented reality, making significant contributions to AI-driven storytelling, gamification, and sustainable design 🌍.

Publication Profile

ORCID

🎓 Academic Background

Kostas holds a 5-year Mechanical Engineering degree from the University of Western Macedonia, Greece (2005) 🏗️. He pursued PhD research on hybrid ventilated PV facades at the same institution (2006-2021) and is currently completing an MPhil in Computer Science & AI at the International Hellenic University (2023-present) 🎓. His latest research focuses on AI multi-agent systems as part of his PhD at the University of the Aegean (2024-present), further cementing his expertise in AI agency and intelligent automation.

💼 Professional Experience

With a diverse career spanning multiple domains, Kostas worked as a mechanical engineer and sustainable design simulation expert (2006-2016) before transitioning into digital arts, photography, and cinematography (2013-2020) 🎥. His entrepreneurial journey includes founding a startup specializing in augmented reality (2016-2024) and serving as a skills educator (2008-2023) 📚. Since 2018, he has been a full-stack developer, focusing on AI applications, web technologies, and vector databases. Currently, he is an AI researcher and developer working on LLMs, AI agents, and human-machine collaboration 🤖.

🏆 Awards and Honors

Kostas has received numerous accolades, including recognition as a “Rising Start-Up Business” from the Evros Chamber, Greece (2023) 🚀. He secured first place in the EU Interreg Greece-Bulgaria Startup Contest (2023) and was a finalist in the Athens StartUp Awards (2018) and XR COSMOS Greece (2021). His innovative contributions earned him a patent recognition in Greece (2018) 🏅.

🔬 Research Focus

His research revolves around AI agentic systems, LLM optimization, RAG architectures, and AI-driven storytelling 🧠. He explores the intersection of generative AI, human-computer collaboration, and augmented reality for educational and creative applications. His work also extends to sustainable AI, assessing the carbon footprint of deep learning models and developing efficient AI architectures for various domains.

📝 Conclusion

Kostas Ordoumpozanis is a visionary AI researcher and innovator, merging technical expertise with creative problem-solving. His contributions to AI agents, storytelling, and sustainable AI showcase his commitment to pushing technological boundaries. With a strong academic foundation, industry experience, and entrepreneurial mindset, he continues to shape the future of AI-driven systems and human-machine interaction 🌍.

📚 Top Research Publications

Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields (2025) – Applied Sciences
Cited by: Multiple AI research articles

Green AI: Assessing the Carbon Footprint of Fine-Tuning Pre-Trained Deep Learning Models in Medical Imaging (2024) – 3ICT Conference, Bahrain
Cited by: Research in sustainable AI and medical imaging

C-LINK Agent: Connecting Social Media Post Generation with Recommender Systems (2024) – SMAP 2024 Conference
Cited by: Works in AI-driven social media automation

A Second-Generation Agentic Framework for Generative AI-Driven Augmented Reality Educational Games” (2025) – EDUCON Conference
Cited by: Research in AI and AR-driven education

Energy, Comfort, and Indoor Air Quality in Nursery and Elementary School Buildings in the Cold Climatic Zone of Greece” (Published in Energy and Buildings)
Cited by: Sustainability and green architecture studies

Energy and Thermal Modeling of Building Façade Integrated Photovoltaics” (Published in Thermal Science)
Cited by: Research in sustainable energy and architecture