Opeyemi Afolabi | Biometrics and Security | Best Scholar Award

Published on by Computer Vision Awards

Mr. Opeyemi Afolabi | Biometrics and Security | Best Scholar Award

Student | Instituto Politecnico Nacional | Mexico

Mr. Opeyemi  Afolabi is a promising researcher whose scholarly endeavors focus on the intersection of chaotic systems, fractional-order modeling, and reconfigurable digital hardware design. His research contributes to advancing the understanding and implementation of complex nonlinear systems in secure communication and intelligent signal processing. With 4 scientific documents, 1citation, and an h-index of 1, his emerging academic profile demonstrates a strong foundation in computational modeling and hardware-oriented system innovation.His recent publications in Fractal and Fractional (MDPI) highlight his growing impact in the field of digital systems and secure image transmission. In FPGA Realization of a Fractional-Order Model of Universal Memory Elements”  and FPGA Implementation of Secure Image Transmission System Using 4D and 5D Fractional-Order Memristive Chaotic Oscillators, Afolabi and his collaborators   including Esteban Tlelo-Cuautle, Jose-Cruz Nuñez-Perez, Vincent-Ademola Adeyemi, and Yuma Sandoval-Ibarra present pioneering FPGA-based realizations of fractional-order systems. These studies merge mathematical theory with hardware efficiency to improve system reliability, encryption strength, and processing speed.Afolabi’s expertise lies in the FPGA implementation of nonlinear circuits, fractional-order chaotic oscillators, and secure digital communication architectures. His research is notable for bridging the theoretical complexity of fractional calculus with practical, hardware-level applications that enhance data security, image integrity, and communication efficiency.The broader societal relevance of his work lies in its potential to strengthen cybersecurity infrastructure, medical imaging reliability, and industrial automation systems. Through innovative system modeling and collaborative research, Afolabi contributes to the global pursuit of secure, energy-efficient, and intelligent digital technologies. His ongoing work reflects a vision of integrating advanced computational paradigms into real-world digital solutions that support technological resilience and global innovation.

Profiles: ORCID |  Scopus

Featured Publications

1. Afolabi, O. M., Adeyemi, V. A., Tlelo-Cuautle, E., & Nuñez-Perez, J.-C. (2024). FPGA realization of a fractional-order model of universal memory elements. Fractal and Fractional, 8(10), 605.

2. Nuñez-Perez, J.-C., Afolabi, O. M., Adeyemi, V. A., Sandoval-Ibarra, Y., & Tlelo-Cuautle, E. (2025). FPGA implementation of secure image transmission system using 4D and 5D fractional-order memristive chaotic oscillators. Fractal and Fractional, 9(8), 506.

Opeyemi Micheal Afolabi’s research advances the frontiers of secure digital communication and hardware intelligence by integrating chaotic and fractional-order systems into FPGA-based architectures. His innovative work enhances the reliability, security, and efficiency of digital technologies, contributing to global progress in cybersecurity, embedded systems, and next-generation communication infrastructure.

Varsha Singh | Deep Learning for Computer Vision | Best Researcher Award

Published on by Computer Vision Awards

Ms. Varsha Singh | Deep Learning for Computer Vision | Best Researcher Award

Research Scholar (Ph.D.) | National Institute of Technology | India

Ms. Varsha Singh is a dedicated researcher at the National Institute of Technology, Tiruchirappalli, specializing in deep learning, computer vision, and efficient image super-resolution architectures. Her research is centered on developing lightweight yet high-performing neural models that enhance perceptual image quality through advanced multi-scale feature extraction, attention mechanisms, and dense connectivity designs.Her notable contribution, Optimized and Deep Cross Dense Skip Connected Network for Single Image Super-Resolution (DCDSCN) published in SN Computer Science introduced a cross-dense skip-connected framework that effectively balances computational efficiency and reconstruction accuracy. The proposed Cross Dense-in-Dense Convolution Block (CDDCB) leverages multi-branch feature fusion and short-path gradient propagation, achieving superior PSNR and SSIM performance across benchmark datasets such as Set5, Set14, BSD100, and Urban100. Building on this foundation, her subsequent work Multi-Scale Attention Residual Convolution Neural Network for Single Image Super-Resolution (MSARCNN) published in Digital Signal Processing Elsevier  advances the field through the integration of Squeeze-and-Excitation and Pixel Attention modules within a multi-scale residual framework, enabling fine-grained texture recovery while maintaining low model complexity.With two international journal publications, Ms. Singh’s work demonstrates a strong emphasis on hierarchical feature fusion, adaptive attention modeling, and efficient neural design for real-time visual intelligence. She actively contributes to the scholarly community as a reviewer for the International Research Journal of Multidisciplinary Technovation (Scopus Indexed), where she has evaluated research papers in deep learning and image processing.Ms. Singh’s contributions bridge theoretical innovation and practical deployment, particularly in resource-constrained imaging and enhancement systems, fostering advancements in next-generation super-resolution and perceptual image restoration. Her research continues to strengthen the global discourse on AI-driven visual computing, supporting the development of intelligent and sustainable imaging solutions for diverse real-world applications.

Profiles: Google Scholar | ResearchGate

Featured Publications

1.Singh, V., Vedhamuru, N., Malmathanraj, R., & Palanisamy, P. (2025). Multi-scale attention residual convolution neural network for single image super-resolution (MSARCNN). Digital Signal Processing, 146, 105614.

2.Singh, V., Vedhamuru, N., Malmathanraj, R., & Palanisamy, P. (2025). Optimized and deep cross dense skip connected network for single image super-resolution (DCDSCN). SN Computer Science, 6(5), 495.

Ms. Varsha Singh’s research advances efficient deep learning and image super-resolution, enabling high-quality visual reconstruction with minimal computational cost. Her innovations contribute to scientific progress in AI-driven imaging, with potential applications in medical diagnostics, remote sensing, and real-time visual enhancement, driving global innovation in sustainable and intelligent vision technologies.

Abrar Alajlan | Deep Learning for Computer Vision | Best Researcher Award

Published on by Computer Vision Awards

Dr. Abrar Alajlan | Deep Learning for Computer Vision | Best Researcher Award

Associate professor | King Saud University | Saudi Arabia

Dr. Abrar Alajlan is an Associate Professor of Computer Science at King Saud University  Saudi Arabia, renowned for his multidisciplinary research contributions across Artificial Intelligence (AI), Machine Learning, Wireless Sensor Networks  Expert Systems, Robotics, and Cloud Computing Security. His academic and scientific work integrates computational intelligence with practical problem-solving, contributing to the advancement of smart adaptive and secure digital ecosystems. Dr. Alajlan has authored 28 peer-reviewed scientific publications and a scholarly book titled Cryptographic Methods His research outputs have achieved over 412 citations, with an h-index of 10 and i10-index of 11, reflecting his consistent impact and scholarly excellence in computer science and AI applications.Among his notable achievements, his paper ESOA-HGRU: Egret Swarm Optimization Algorithm-Based Hybrid Gated Recurrent Unit for Classification of Diabetic Retinopathy published in Artificial Intelligence Review is ranked in the Top 5% of ISI journals, showcasing his pioneering efforts in applying optimization-based deep learning for medical diagnostics. His other influential works, including A Novel-Cascaded ANFIS-Based Deep Reinforcement Learning for the Detection of Attacks in Cloud IoT-Based Smart City Applications Concurrency and Computation: Practice and Experience and Artificial Intelligence-Based Multimodal Medical Image Fusion Using Hybrid S2 Optimal CNN demonstrate his commitment to bridging AI with cybersecurity healthcare and intelligent automation.Earlier in his career Dr. Alajlan’s significant contributions to robotics and sensor-based systems notably  Trajectory Planning and Collision Avoidance Algorithm for Mobile Robotics Systems IEEE Sensors Journal and Sensor Fusion-Based Model for Collision-Free Mobile Robot Navigation earned substantial citations and remain foundational in the field of autonomous robotic navigation and path optimization.Dr. Alajlan’s extensive collaborations with leading researchers such as M. M. Almasri, K. M. Elleithy and A. Razaque have resulted in high-impact publications addressing challenges in smart cities network security and intelligent automation. His research stands out for its societal relevance, focusing on AI-driven healthcare solutions, sustainable IoT systems, and secure digital transformation. Through his scholarly excellence, mentorship, and interdisciplinary approach, Dr. Alajlan continues to advance the frontiers of intelligent computing for global scientific and technological progress.

Profiles: Google Scholar | Scopus | ResearchGate

Featured Publications

1.Almasri, M. M., Alajlan, A. M., & Elleithy, K. M. (2016). Trajectory planning and collision avoidance algorithm for mobile robotics system. IEEE Sensors Journal, 16(12), 5021–5028. Cited By : 89

2.Almasri, M., Elleithy, K., & Alajlan, A. (2015). Sensor fusion-based model for collision-free mobile robot navigation. Sensors, 16(1), 24. Cited By : 76

3.Almasri, M. M., Elleithy, K. M., & Alajlan, A. M. (2016, May). Development of efficient obstacle avoidance and line following mobile robot with the integration of fuzzy logic system in static and dynamic environments. In 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT) (pp. 1–6). IEEE. Cited By : 30

4.Alajlan, A. M., Almasri, M. M., & Elleithy, K. M. (2015, May). Multi-sensor based collision avoidance algorithm for mobile robot. In 2015 Long Island Systems, Applications and Technology Conference (pp. 1–6). IEEE. Cited By : 30

5.Almasri, M. M., & Alajlan, A. M. (2022). Artificial intelligence-based multimodal medical image fusion using hybrid S2 optimal CNN. Electronics, 11(14), 2124. Cited By : 25

Dr. Abrar M. Alajlan’s pioneering research in Artificial Intelligence and secure computational systems bridges scientific innovation with real-world applications, advancing intelligent healthcare, smart city resilience, and cyber-secure digital infrastructures. His vision centers on harnessing AI to create adaptive, safe, and sustainable technologies that empower global innovation and societal well-being.

Paulo Dias | Augmented Reality (AR) and Virtual Reality (VR) | Best Researcher Award

Published on by Computer Vision Awards

Prof. Paulo Dias | Augmented Reality (AR) and Virtual Reality (VR) | Best Researcher Award

Auxiliar with Habilitation | University of Aveiro | Portugal

Prof. Paulo Dias is a distinguished researcher at the University of Aveiro, Portugal, whose pioneering work spans 3D reconstruction, computer vision, virtual and augmented reality (VR/AR), human–computer interaction (HCI), and robotics. With an extensive record of over 506 scientific publications, he has achieved 4,802 total citations, an h-index of 33, and an i10-index of 128, reflecting a profound and sustained influence on the global research community.His work has notably advanced immersive technologies and their applications in healthcare, education, and industrial environments. Among his most cited studies is Head-mounted display versus desktop for 3D navigation in virtual reality Multimedia Tools and Applications, a landmark comparative study on immersive interaction. His research on Using virtual reality to increase motivation in post-stroke rehabilitation IEEE Computer Graphics and Applications, demonstrates his commitment to applying VR to rehabilitation and assistive technologies, enhancing patient engagement and recovery outcomes.Prof. Dias has also co-authored the influential A conceptual model and taxonomy for collaborative augmented reality IEEE Transactions on Visualization and Computer Graphics, providing a robust framework for understanding and designing AR-based collaborative systems. His contributions to autonomous vehicle sensor calibration, situated visualization for decision-making, and remote collaboration in industrial contexts further illustrate his multidisciplinary impact and innovation-driven research agenda.Through collaborative projects with experts such as B. Sousa Santos and B. Marques, Prof. Dias continues to bridge the gap between technological innovation and human experience, integrating digital environments with real-world problem-solving. His body of work not only advances scientific understanding but also fosters societal progress through the development of intelligent, accessible, and immersive technologies that redefine how humans interact with digital information and environments.

Profiles: Google Scholar | ORCID | ResearchGate

Featured Publications

1.Madeira, T., Oliveira, M., & Dias, P. (2025). Reflection-aware 3D mirror segmentation and pose estimation.
Cited By : 2

2. Oliveira, S., Marques, B., Amorim, P., Dias, P., & Sousa Santos, B. (2024). Stepping into recovery with an immersive virtual reality serious game for upper limb rehabilitation: A supermarket experience for stroke survivors. In International Conference on Human-Computer Interaction . Cited By : 13

3. Madeira, T., Oliveira, M., & Dias, P. (2024). Neural colour correction for indoor 3D reconstruction using RGB-D data. Sensors, 24(13), 4141. Cited By : 3

4. Maio, R., Araújo, T., Marques, B., Santos, A., Ramalho, P., Almeida, D., & Dias, P. (2024). Pervasive augmented reality to support real-time data monitoring in industrial scenarios: Shop floor visualization evaluation and user study. Computers & Graphics, 118, 11–22. Cited By : 30

5. Marques, B., Silva, S., Maio, R., Dias, P., & Sousa Santos, B. (2024). Guidelines for designing mixed reality solutions in remote scenarios. In International Conference on Human-Computer Interaction. Cited By : 4

Prof. Paulo Dias’s pioneering research in 3D vision, virtual and augmented reality, and human–computer interaction is transforming how humans engage with digital environments. His innovations bridge science and society by driving advancements in healthcare rehabilitation, immersive learning, and industrial automation, fostering a more intelligent and inclusive digital future.

Alina Diana Zamfir | Biomedical and Healthcare Applications | Best Researcher Award

Published on by Computer Vision Awards

Prof. Alina Diana Zamfir | Biomedical and Healthcare Applications | Best Researcher Award

Professor |  National Institute for R&D in Electrochemistry | Romania

Prof. Dr. Alina D. Zamfir is a leading Romanian scientist recognized internationally for her pioneering research in mass spectrometry, glycomics, proteomics, and structural biology. She currently holds dual appointments as Senior Scientific Researcher at the National Institute for Research and Development in Electrochemistry and Condensed Matter Research, Timisoara, and as Professor at Aurel Vlaicu University of Arad, Romania. she has also served as a PhD Supervisor at the Faculty of Physics, West University of Timisoara, mentoring numerous young scientists in advanced analytical methodologies.Prof. Zamfir’s research has made seminal contributions to the development of advanced mass spectrometry platforms, particularly in the integration of microfluidics, ion mobility, and nanoelectrospray systems for glycoproteomics and glycolipidomics. She has been the Principal Investigator of 17 national and international projects, funded by the Romanian UEFISCDI and the European Union, focusing on the structural and functional elucidation of complex biological molecules, including gangliosides and proteoglycans. Her collaborations extend across prestigious institutions such as the University of Münster, University of Konstanz (Germany), and Clarkson University (USA), contributing significantly to global biomedical research.Author of 198 peer-reviewed publications with over 3,018 citations and an h-index of 37, Prof. Zamfir’s work has appeared in high-impact journals including Analytical Chemistry, Electrophoresis, Glycobiology, and the Journal of the American Society for Mass Spectrometry. She has also served as Guest Editor for Molecules and Frontiers in Molecular Biosciences and as President of the Romanian Society for Mass Spectrometry since 2009, promoting the advancement of analytical sciences in Romania and beyond.Through her academic leadership, editorial contributions, and innovative research, Prof. Zamfir has profoundly influenced modern bioanalytical chemistry, driving forward applications in biomarker discovery, neurodegenerative disease research, and precision medicine. Her work bridges fundamental science and societal impact, advancing both Romania’s scientific excellence and the global progress of molecular biosciences.

Profiles: Google Scholar | ResearchGate

Featured Publications

1, Zamfir, A. D. (2007). Recent advances in sheathless interfacing of capillary electrophoresis and electrospray ionization mass spectrometry. Journal of Chromatography A, 1159(1–2), 2–13. Cited By : 117

2. Zamfir, A., Vakhrushev, S., Sterling, A., Niebel, H. J., Allen, M., & Peter-Katalinić, J. (2004). Fully automated chip-based mass spectrometry for complex carbohydrate system analysis. Analytical Chemistry, 76(7), 2046–2054.
Cited By : 94

3. Sarbu, M., Robu, A. C., Ghiulai, R. M., Vukelić, Z., Clemmer, D. E., & Zamfir, A. D. (2016). Electrospray ionization ion mobility mass spectrometry of human brain gangliosides. Analytical Chemistry, 88(10), 5166–5178. Cited By : 81

4. Sisu, E., Flangea, C., Serb, A., & Zamfir, A. D. (2011). Modern developments in mass spectrometry of chondroitin and dermatan sulfate glycosaminoglycans. Amino Acids, 41(2), 235–256. Cited By : 68

5. Zamfir, A. D., Bindila, L., Lion, N., Allen, M., Girault, H. H., & Peter-Katalinić, J. (2005). Chip electrospray mass spectrometry for carbohydrate analysis. Electrophoresis, 26(19), 3650–3673.* Cited By : 66

Shakil Hossain | Multi-Modal and Cross-Modal Vision | Young Scientist Award

Published on by Computer Vision Awards

Mr. Md. Shakil Hossain | Multi-Modal and Cross-Modal Vision | Young Scientist Award

Research Assistant | Bangladesh University of Business and Technology | Bangladesh 

Md. Shakil Hossain is an emerging researcher and academic specializing in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), and Multimodal Learning. He currently serves as a Research Assistant at the Advanced Machine Intelligence Research (AMIR) Lab, where his work focuses on hybrid deep learning architectures, large language models (LLMs), and multimodal fusion systems for real-world AI applications. His research aims to bridge the gap between intelligent computation and societal needs, with contributions spanning sentiment analysis, mental health assessment, and cross-lingual text processing.Before joining AMIR Lab, he worked as a Market Research Analyst at Gram Ltd., where he conducted in-depth market and competitive analyses to support the launch of Dhopa Elo, an innovative startup product revolutionizing laundry services. He also utilized data analytics, customer segmentation, and ROI optimization to strengthen marketing strategies and business performance.Md. Hossain received a research grant from the Bangladesh University of Business and Technology (BUBT) for his project, Smart Agro-Monitor: IoT-Based Precision Farming for Enhanced Crop Management.” This initiative leveraged IoT and AI to improve water management, pest control, and crop health monitoring, empowering farmers with data-driven insights for sustainable agriculture.He has authored and co-authored 16 research papers in leading journals and conferences such as Scientific Reports, IEEE Access, Knowledge-Based Systems, and Neural Networks. His publications have collectively received 31 citations, with an h-index of 3 and an i10-index of 1, reflecting his growing academic impact. Collaborating with renowned scholars including Prof. Dr. A. B. M. Shawkat Ali, Md. Hossain continues to pursue interdisciplinary AI research that promotes innovation, ethics, and societal advancement through intelligent technologies.

Profiles: Google Scholar | ORCID  | Scopus

Featured Publications

1.Hossain, M. M., Hossain, M. S., Mridha, M. F., Safran, M., & Alfarhood, S. (2025). Multi-task opinion enhanced hybrid BERT model for mental health analysis.  Cited By: 13

2.Hossain, M. M., Hossain, M. S., Hossain, M. S., Mridha, M. F., & Safran, M. (2024). TransNet: Deep attentional hybrid transformer for Arabic posts classification. IEEE Access. Cited By: 7

3.Hossain, M. M., Hossain, M. S., Safran, M., Alfarhood, S., & Alfarhood, M. (2024). A hybrid attention-based transformer model for Arabic news classification using text embedding and deep learning. IEEE Access. Cited By: 6

4.Hossain, M. M., Hossain, M. S., Chaki, S., Hossain, M. R., Rahman, M. S., & Ali, A. B. M. (2025). CrosGrpsABS: Cross-attention over syntactic and semantic graphs for aspect-based sentiment analysis in a low-resource language.  Cited By: 2

5.Hossain, M. S., Hossain, M. M., Hossain, M. S., Mridha, M. F., & Safran, M. (2025). EmoNet: Deep attentional recurrent CNN for X (formerly Twitter) emotion classification. IEEE Access. Cited By: 2

Md. Shakil Hossain’s research advances the integration of AI, NLP, and IoT to solve real-world problems in healthcare, agriculture, and digital communication. His work promotes human-centered, sustainable, and data-driven innovation, empowering industries and societies to harness intelligent technologies for global progress.

Nagaraj | Deep Learning for Computer Vision | Excellence in Research

Published on by Computer Vision Awards

Dr. P. Nagaraj | Deep Learning for Computer Vision | Excellence in Research

Associate Professor | SRM Institute of Science and Technology  | India 

Dr. P. Nagaraj is an esteemed Associate Professor at the SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India. With research expertise spanning Artificial Intelligence, Data Science, Data Analytics, Machine Learning, and Recommender Systems, he has made substantial contributions to intelligent computing and healthcare analytics. His innovative work focuses on applying deep learning, fuzzy inference, and explainable AI (XAI) techniques to real-world challenges in medical diagnosis, cybersecurity, and sustainable automation.Dr. Nagaraj has an impressive research portfolio, with over 208 indexed publications, 2,736 citations, and an h-index of 32, reflecting the global relevance and scholarly influence of his work. His notable publications include advancements in diabetes prediction, brain tumor classification, Alzheimer’s disease analysis, and cyberattack detection using AI-driven frameworks. His studies on distributed denial-of-service (DDoS) detection, IoT-based healthcare systems, and intelligent recommendation models have been widely cited and applied across multiple interdisciplinary domains.In recognition of his outstanding research, Dr. Nagaraj has been consecutively listed among the World’s Top 2% Scientists (2023–2025), highlighting his sustained impact in computer science and data-driven innovation. He is also a two-time recipient of the prestigious India AI Fellowship (Ministry of Electronics and Information Technology, MeitY), each worth ₹1 Lakh, for his pioneering projects titled AgriTech of Next-Gen Automation for Sustainable Crop Production and A Deep Learning Approach to Improve Pulmonary Cancer Diagnosis Using CNN.Through collaborations with national and international scholars, Dr. Nagaraj continues to advance the frontier of intelligent data analytics for societal benefit. His research contributes significantly to sustainable digital transformation, healthcare improvement, and agricultural innovation, positioning him as a leading figure in India’s AI research landscape and a global advocate for technology-driven social progress.

Profiles: Google Scholar | ORCID  | Scopus

Featured Publications

1.Sudar, K. M., Beulah, M., Deepalakshmi, P., Nagaraj, P., & Chinnasamy, P. (2021). Detection of distributed denial of service attacks in SDN using machine learning techniques. In Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–6). IEEE. Cited By : 158

2.Nagaraj, P., & Deepalakshmi, P. (2022). An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis. International Journal of Imaging Systems and Technology, 32(4), 1373–1396. Cited By : 100

3.Nagaraj, P., Muneeswaran, V., Reddy, L. V., Upendra, P., & Reddy, M. V. V. (2020). Programmed multi-classification of brain tumor images using deep neural network. In Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1–6). IEEE. Cited By : 85

4.Nagaraj, P., Deepalakshmi, P., & Romany, F. M. (2021). Artificial flora algorithm-based feature selection with gradient boosted tree model for diabetes classification. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 2789–2802. Cited By : 79

.5.Nagaraj, P., & Deepalakshmi, P. (2020). A framework for e-healthcare management service using recommender system. Electronic Government, an International Journal, 16(1–2), 84–100. Cited By : 70

Dr. P. Nagaraj’s research advances global innovation by integrating artificial intelligence and data analytics to address critical challenges in healthcare, agriculture, and cybersecurity. His vision is to harness intelligent automation and explainable AI to create sustainable, data-driven solutions that enhance human well-being, industrial efficiency, and societal resilience.

Catalin Dumitrescu | Biometrics and Security | Best Researcher Award

Published on by Computer Vision Awards

Prof. Catalin Dumitrescu | Biometrics and Security | Best Researcher Award

Prof. Habil. Artificial Intelligence | University Politehnica of Bucharest | Romania

Assoc. Prof. Dr. Catalin Dumitrescu is a distinguished researcher and academic specializing in Artificial Intelligence (AI), Digital Signal Processing (DSP), and Machine Learning (ML) with a strong interdisciplinary focus on computer vision, cognitive radio, cyber defence, and multimedia security. His research integrates advanced AI algorithms into industrial electronics, telecommunications, and defence technologies, with a particular emphasis on IMINT/SIGINT systems and cyber defence infrastructures.With an impressive research portfolio comprising over 50 scientific publications, his work has garnered 536 citations, an h-index of 10, and i10-index of 15, reflecting his growing influence in the fields of intelligent systems and adaptive signal processing. Dr. Dumitrescu’s publications in leading journals such as Sensors, Electronics, Applied Sciences, and Fractal and Fractional (MDPI) highlight his expertise in deep learning, visual classification, object detection, and decision-making algorithms. His recent studies focus on AI-driven noise reduction, fractal-based steganography for data security, and UAV detection systems using sensor data fusion and fuzzy logic.His research interests span a wide spectrum, including neural networks for image and audio processing, machine learning-based EEG signal classification, brain-computer interfaces, digital watermarking and cryptography, and real-time signal and image analysis. Through collaborations with academia and industry, he has contributed to the development of automated, intelligent systems for security, communication, and transportation applications, bridging theoretical innovation with practical deployment.Dr. Dumitrescu’s commitment to advancing AI and DSP research extends to mentoring and consultancy, where he collaborates with organizations across industrial electronics, telecommunication, and defence sectors. His work has had a significant societal impact in enhancing the reliability, efficiency, and security of next-generation digital systems. His contributions continue to shape the global discourse on intelligent signal processing, autonomous systems, and secure information technologies.

Profiles: Google Scholar | ORCID 

Featured Publications

Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

Published on by Computer Vision Awards

Dr. Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

Assistant Researcher | Zhejiang Lab | China

Dr. Xiangfu Kong is a distinguished researcher at Zhejiang Lab, specializing in intelligent transportation systems (ITS), spatiotemporal data analytics, and urban mobility optimization. His work bridges computer science, artificial intelligence, and transportation engineering to develop data-driven models that enhance mobility efficiency safety, and sustainability in smart cities.With an Publications 6  h-index of 3, and 67 citations across recognized publications, Dr. Kong has made notable scholarly contributions to the field. He has published six peer-reviewed research articles, including influential works such as “Measuring Traffic Congestion with Taxi GPS Data and Travel Time Index and  A Scenario-Based Map-Matching Algorithm for Complex Urban Road Networks. His recent studies explore flood risk mapping, travel time reliability, and natural language processing for urban data interpretation, showcasing his interdisciplinary expertise.Dr. Kong’s research projects often involve large-scale real-world data, particularly GPS-based urban mobility and hydrological data, integrating AI algorithms and Bayesian frameworks to model and predict transportation dynamics under diverse conditions. His studies have direct implications for urban policy-making, disaster management, and infrastructure resilience.He has actively collaborated with industry and academic partners to design computational models that assist in traffic monitoring, path planning, and flood management, contributing to sustainable urban development initiatives. Dr. Kong’s innovative use of AI for understanding urban systems highlights his dedication to applying research outcomes to societal benefit.In addition to his publications, Dr. Kong contributes to the broader scientific community through editorial and peer-review roles in transportation and data science journals. His ongoing work in data-driven transportation intelligence and urban informatics positions him as a promising researcher contributing to the next generation of smart mobility systems.Through his research excellence and cross-disciplinary collaborations, Dr. Xiangfu Kong continues to push the boundaries of how AI and data analytics can transform urban transportation, improve public safety, and drive global sustainability efforts.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Kong, X., Yang, J., & Yang, Z. (2015). Measuring traffic congestion with taxi GPS data and travel time index. Proceedings of the CICTP 2015, 3751–3762. Cited By : 35

2. Kong, X., & Yang, J. (2019). A scenario-based map-matching algorithm for complex urban road network. Journal of Intelligent Transportation Systems, 23(6), 617–631.
Cited By : 19

3. Kong, X., Yang, J., Qiu, J., Zhang, Q., Chen, X., Wang, M., & Jiang, S. (2022). Post‐event flood mapping for road networks using taxi GPS data. Journal of Flood Risk Management,  Cited By : 8

4. Xiangfu, K., Bo, D., Xu, K., & Yongliang, T. (2023). Text classification model for livelihood issues based on BERT: A study based on hotline compliant data of Zhejiang province. Acta Scientiarum Naturalium Universitatis Pekinensis, 59(3), 456–466. Cited By : 3

5. Kong, X., & Yang, J. (2016). Path planning with information on travel time reliability. Proceedings of the CICTP 2016, 99–107. Cited By :  2

6. Kong, X., Yang, J., Xu, K., Dong, B., & Jiang, S. (2023). A Bayesian updating framework for calibrating hydrological parameters of road network using taxi GPS data. Hydrology and Earth System Sciences Discussions, 1–25.

Dr. Xiangfu Kong nresearch advances data-driven intelligent transportation and urban informatics, fostering safer, more efficient, and sustainable mobility systems. His innovative integration of AI, GPS analytics, and hydrological modeling contributes to scientific progress, climate-resilient infrastructure, and smart city innovation with lasting global impact.

Simy Baby | Applications of Computer Vision | Best Researcher Award

Published on by Computer Vision Awards

Mrs. Simy Baby | Applications of Computer Vision | Best Researcher Award

Researcher | National Institute of Technology | India

Mrs. Simy Baby is a pioneering researcher at the National Institute of Technology, Tiruchirappalli, with extensive expertise in machine learning, semantic communication, computer vision, and mmWave radar signal processing. Her research bridges the gap between radar sensing and intelligent communication frameworks, focusing on efficient feature extraction, complex-valued encoding, and task-oriented inference.Her seminal work, “Complex Chromatic Imaging for Enhanced Radar Face Recognition” (Computers and Electrical Engineering,  introduced a novel representation that preserves amplitude and phase information of mmWave radar signals, achieving an exceptional recognition accuracy. Another significant contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication” (IEEE Transactions on Cognitive Communications and Networking ), proposed a CLDA-based encoding framework enhancing feature interpretability and robustness under channel variations. Current investigations include Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, highlighting her commitment to multitask semantic communication systems.Dr. Baby has 21 publications with 20 citations and an h-index of 3.  demonstrating a rapidly growing impact in her field. She is an active member of the Indian Society for Technical Education (ISTE) and contributes to the scientific community through innovative research that combines theory and practical applications. Her work on radar-based recognition, semantic feature transmission, and multi-task inference frameworks holds significant potential for intelligent transportation systems, human activity recognition, and bandwidth-efficient communication technologies.Through her research, Dr. Baby has established herself as a leading figure in advancing radar imaging and semantic communication, providing scalable solutions that merge high-performance computing with real-world societal applications. Her vision continues to shape the future of intelligent sensing and communication systems globally.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Ansal, K. A., Rajan, C. S., Ragamalika, C. S., & Baby, S. M. (2022). A CPW fed monopole antenna for UWB/Ku band applications. Materials Today: Proceedings, 51, 585–590. Cited By : 5

2. Ansal, K. A., Ragamalika, C. S., Rajan, C. S., & Baby, S. M. (2022). A novel ACS fed antenna with comb shaped radiating strip for triple band applications. Materials Today: Proceedings, 51, 332–338. Cited By : 4

3. Ansal, K. A., Kumar, A. S., & Baby, S. M. (2021). Comparative analysis of CPW fed antenna with different substrate material with varying thickness. Materials Today: Proceedings, 37, 257–264. Cited By : 4

4. Baby, S. M., & Gopi, E. S. (2025). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering, 123, 110198. Cited By : 3

5.Ansal, K. A., Shanmuganatham, T., Baby, S. M., & Joy, A. (2015). Slot coupled microstrip antenna for C and X band application. International Journal of Advanced Research Trends in Engineering and Technology.Cited By : 3

Dr. Simy M. Baby’s research advances the integration of semantic communication and computer vision, enabling high-accuracy radar-based recognition and task-oriented inference. Her work has significant implications for intelligent transportation, human activity monitoring, and bandwidth-efficient communication, driving innovation in both science and industry globally.