Muhammad Bilal | Computer Vision for Robotics and Autonomous Systems | Editorial Board Member

Published on by Computer Vision Awards

Assoc. Prof. Dr. Muhammad Bilal | Computer Vision for Robotics and Autonomous Systems | Editorial Board Member

Gunagzhou Nanfang College | China

Dr. Muhammad Bilal is an Associate Professor at Nanfang College, China, specializing in artificial intelligence, machine learning, and underwater acoustic communication. His research focuses on bio-inspired covert communication, low probability detection systems, and AI-driven signal processing, with applications in marine technology, cybersecurity, and healthcare. He has authored numerous peer-reviewed publications in leading international journals and conferences. Dr. Bilal actively collaborates with global research communities and serves as a reviewer for reputed journals. His work advances secure communication systems and contributes to the development of sustainable and intelligent ocean technologies with broad societal impact.

Citation Metrics (Scopus)

600

400

200

0

Citations
381

Documents
41

h-index
11

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile    View ORCID Profile
     View Google Scholar  Profile
     View ResearchGate Profile

Featured Publications


Biologically inspired covert underwater acoustic communication—A review.

–Physical Communication, 30, 107–114. (2018). Cited By: 55

A frequency hopping pattern inspired bionic underwater acoustic communication.

– Physical Communication, 46, 101288. (2021). Cited By: 43

Sheilla Ann Pacheco | Machine Learning for Computer Vision | Editorial Board Member

Published on by Computer Vision Awards

Assist. Prof. Dr. Sheilla Ann Pacheco | Machine Learning for Computer Vision | Editorial Board Member

Faculty | North Eastern Mindanao State University | Philippines

Sheilla Ann B. Pacheco is an Assistant Professor II of Computer Science at North Eastern Mindanao State University, Philippines. Her research focuses on image processing, machine learning, computer vision, and AI-driven healthcare applications. She has authored multiple peer-reviewed journal and conference publications, with works appearing in international venues such as Procedia Computer Science, International Journal of Computers and Applications, and IEEE conferences. Her studies address content-based image retrieval, facial biometrics, adversarial attacks, and ensemble learning for disease prediction. Through interdisciplinary collaborations, her research contributes to advancing robust AI systems with practical societal impact in healthcare, education, and security domains.

 

Citation Metrics (Scopus)

30

20

10

5

0

Citations
9

Documents
8

h-index
2

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View ORCID Profile
        View Google Scholar Profile

Featured Publications


Enhanced content-based image retrieval using multivisual features fusion.

– International Journal of Computers and Applications. (2025). Cited By : 4

Robust face recognition under adversarial attack using SARGAN model and improved cross triple MobileNetV1.

– In K. Arai (Ed.), Advances in Information and Communication: Proceedings of the Future of Information and Communication Conference (pp. 491–510). Springer. (2025). Cited By: 2

A comprehensive survey on federated learning and its applications in health care.

– In Proceedings of the 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (pp. 407–412). IEEE.. (2024). Cited By: 1

Zhao Zhao | Computer Vision for Robotics and Autonomous Systems | Research Excellence Award

Published on by Computer Vision Awards

Mr. Zhao Zhao | Computer Vision for Robotics and Autonomous Systems | Research Excellence Award

Tianjin University | Canada

Mr. Zhao Zhao is a researcher at Tianjin University specializing in intelligent transportation systems, autonomous mobile robot (AMR) scheduling, and sustainable urban logistics. His work focuses on optimization models integrating energy consumption, time-dependent electricity pricing, and real-world operational constraints. He has published in leading journals including Transportation Research Part C and International Journal of Production Research. Zhao has collaborated with international institutions such as Tsinghua University, Sichuan University, Tilburg University, and KEDGE Business School, and with industry partners including Toyota Tianjin and Hikvision, contributing to large-scale AMR deployment and innovative last-mile delivery solutions with significant societal and sustainability impact.

Documents Metrics (ORCID)

4

3

2

1

0

Documents
1

🟥 Documents

View ORCID Profile

Featured Publications


In-plant autonomous mobile robot scheduling and routing problem considering battery consumption model.

– Transportation Research Part C: Emerging Technologies  (2025). Cited By : 1

Tao Chen | Object Detection and Recognition | Research Excellence Award

Published on by Computer Vision Awards

Dr. Tao Chen | Object Detection and Recognition | Research Excellence Award

Professor | Fudan University | China

Dr. Tao Chen is a leading researcher at Fudan University, specializing in deep learning and computer vision, with a focus on human motion understanding, 3D shape generation, and semantic segmentation. He has contributed to over 249 high-impact publications in top-tier venues including CVPR, NeurIPS, and IEEE Transactions, accumulating more than 6294 citations. His work integrates advanced neural architectures, motion diffusion, and cross-domain adaptation techniques, often in collaboration with international researchers such as G. Yu and W. Liu. Dr. Chen’s research has significant societal impact, advancing intelligent systems for medical imaging, autonomous perception, and interactive 3D applications, bridging fundamental AI research with practical real-world solutions.

Citation Metrics (Google Scholar)

4000

3000

2000

1000

0

Citations
6294

Documents
249

h-index
41

🟦 Citations 🟥 Documents 🟩 h-index

View Google Scholar Profile
           View Research Gate Profile

Featured Publications


Executing your commands via motion diffusion in latent space.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . (2023). Cited By : 580

TopFormer: Token pyramid transformer for mobile semantic segmentation.

-In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2022). Cited By: 388

b‑DARTS: Beta‑decay regularization for differentiable architecture search.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2022). Cited By: 194

LL3DA: Visual interactive instruction tuning for omni‑3D understanding, reasoning, and planning.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2024). Cited By: 178

Zeng Gao | Applications of Computer Vision | Research Excellence Award

Published on by Computer Vision Awards

Dr. Zeng Gao | Applications of Computer Vision | Research Excellence Award

Lecturer | Henan University of Technology | China 

Dr. Zeng Gao is a researcher at Henan University of Technology specializing in machine learning, image processing, and visual tracking. His work focuses on intelligent optimization–driven visual tracking and motion analysis, with influential contributions to abrupt and long-term tracking. He has published over 12 peer-reviewed papers in leading international journals and conferences, including IEEE Access, Expert Systems with Applications, Applied Soft Computing, Digital Signal Processing, and PRCV, accumulating 98 citations. He has participated in two National Natural Science Foundation of China projects and holds three granted invention patents. Dr. Gao actively collaborates with domestic and international institutions, serves as a reviewer for journals such as ACM TOMM and Digital Signal Processing, and contributes to advancing intelligent perception technologies with real-world societal impact.

 

Citation Metrics (Scopus)

200

150

100

50

0

Citations
98

Documents
12

h-index
7

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View ORCID Profile
      View Google Scholar Profile

Featured Publications


Visual tracking with levy flight grasshopper optimization algorithm.

– Pattern Recognition and Computer Vision: Second Chinese Conference, PRCV  (2019). Cited By : 19

Şifa Gül Demiryürek | Generative Models for Computer Vision | Outstanding Scientist Award

Published on by Computer Vision Awards

Dr. Şifa Gül Demiryürek | Generative Models for Computer Vision | Outstanding Scientist Award

Lecturer | Aksaray University | Turkey

Dr. Şifa Gül Demiryürek is a researcher specializing in acoustics, dynamics, vibration control, nonlinear structures, and metamaterials, with a growing body of work that bridges fundamental mechanics and applied engineering. Her research focuses on low-frequency broadband vibration damping, nonlinear passive particle dampers, and metamaterial-inspired structures aimed at improving stability, efficiency, and durability in modern mechanical systems.She has authored 11 scientific documents, accumulating 19 citations with an h-index of 3, reflecting the emerging impact of her contributions. Her early work includes the experimental study of thermal-mixing phenomena in coaxial jets published in the Journal of Thermophysics and Heat Transfer demonstrating her multidisciplinary foundation in fluid–thermal interactions. Transitioning toward structural dynamics  her doctoral research at the University of Sheffield advanced the understanding of periodically arranged nonlinear particle dampers under low-amplitude excitation providing new insights into damping mechanisms critical for lightweight and high-performance structures.Dr. Demiryürek has collaborated with notable researchers such as A. Krynkin and J. Rongong contributing to recognized venues including DAGA, ACOUSTICS Proceedings, and the Institute of Acoustics. Her studies on metamaterial-based dampers and locally resonating structures highlight innovative strategies for vibration mitigation particularly in the low-frequency regime where traditional dampers are less effective. Her works further expand this direction with investigations on dynamic behavior of thermoplastics and material resonance considerations for wind turbine towers addressing contemporary engineering challenges related to sustainability and structural reliability.In addition to research publications she has contributed educational materials including Introduction to Metamaterials  supporting broader knowledge dissemination in emerging engineering domains. Her collaborations in applied mechanics such as the numerical evaluation of electric motorcycle chassis demonstrate a commitment to integrating theoretical advances into practical real-world applications.Through her focused work at the intersection of vibration engineering and metamaterial science Şifa Gül Demiryürek is contributing to next-generation solutions for safer quieter and more efficient mechanical systems with potential societal impact across manufacturing transportation renewable energy and advanced materials engineering.

Profiles: Googlescholar | Scopus | ORCID

Featured Publications

1.Demiryürek, S. G., Kok, B., Varol, Y., Ayhan, H., & Oztop, H. F. (2018). Experimental investigation of thermal-mixing phenomena of a coaxial jet with cylindrical obstacles. Journal of Thermophysics and Heat Transfer, 32(2), 273–283. Cited By: 5

2. Demiryürek, S. G. (2022). Periodically arranged nonlinear passive particle dampers under low-amplitude excitation (Doctoral research, University of Sheffield). Cited By: 3

3. Demiryürek, S. G., & Krynkin, A. (2021). Low-frequency broadband vibration damping using the nonlinear damper with metamaterial properties. In DAGA 2021 Conference Proceedings (pp. 94–96). Cited By: 3

4.Demiryürek, S. G., Krynkin, A., & Rongong, J. (2020). Modelling of nonlinear dampers under low-amplitude vibration. In ACOUSTICS 2020 Proceedings. Cited By: 3

5.Demiryürek, S. G., Krynkin, A., & Rongong, J. (2019). Non-linear metamaterial structures: Array of particle dampers. Universitätsbibliothek der RWTH Aachen. Cited By: 3

Dr. Şifa Gül Demiryürek’s research advances next-generation vibration damping and metamaterial technologies, enabling safer, quieter, and more efficient mechanical systems across industries. Her contributions support innovation in sustainable engineering from wind energy structures to lightweight transportation strengthening global efforts toward resilient, high-performance designs.

Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

Published on by Computer Vision Awards

Assoc. Prof. Dr. Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

Associate Professor | Shiraz University | Iran

Dr. Mohsen Edalat an accomplished researcher from Shiraz University, Iran, has made notable contributions to the fields of machine learning geospatial modeling and smart agriculture. With an impressive research record comprising 39 scientific publications and over 614 citations Dr. Edalat has demonstrated sustained academic productivity and influence in computational and environmental sciences. His research emphasizes the integration of advanced data-driven algorithms with ecological and agricultural systems to enhance sustainability and decision-making processes.Among his recent works Dr. Edalat has explored diverse applications of machine learning for ecological and agricultural optimization. His 2025 publications include studies on predicting nepetalactone accumulation in Nepeta persica through machine learning and geospatial analysis modeling ecological preferences of Kentucky bluegrass under varying water conditions (Water Switzerland)  and mapping early-season dominant weeds using UAV-based imagery to support precision farming. These investigations reflect his innovative approach to merging remote sensing artificial intelligence and environmental modeling to address complex agroecological challenges.With an h-index of 11 and collaborations with more than 60 co-authors  Dr. Edalat’s work highlights strong interdisciplinary engagement and a commitment to advancing data-driven sustainability. His studies contribute not only to the scientific community but also to practical agricultural applications that promote resource efficiency and ecological resilience. Through his ongoing research Dr. Edalat continues to shape the evolving landscape of smart agriculture and environmental informatics demonstrating the global relevance and societal value of computational intelligence in natural systems.

Profiles:  Scopus | ORCID

Featured Publications

1. Edalat, M., et al. (2025). Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis. Scientific Reports.

2. Edalat, M., et al. (2025). Modeling the ecological preferences and adaptive capacities of Kentucky bluegrass based on water availability using various machine learning algorithms. Water (Switzerland).

3. Edalat, M., et al. (2025). Early season dominant weed mapping in maize field using unmanned aerial vehicle (UAV) imagery: Towards developing prescription map. Smart Agricultural Technology.

Dr. Mohsen Edalat’s research integrates machine learning, geospatial analytics, and agricultural science to enhance crop management and environmental sustainability. His innovative work advances precision agriculture, supporting data-driven decisions that improve resource efficiency, boost food security, and promote sustainable development at a global scale.

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.

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.

Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

Published on by Computer Vision Awards

Prof. Dr. Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

Professor | University of St Andrews | United Kingdom

Prof. Dr. Venkataraman Thangadurai is a globally renowned expert in solid-state chemistry, electrochemical energy storage, and advanced battery technologies. With a research focus on fast ion conductors, solid electrolytes, lithium- and sodium-based batteries, and fuel cell materials, he has made pioneering contributions to both fundamental science and practical energy solutions. Prof. Thangadurai has authored over 278 peer-reviewed journal articles, 6 book chapters, and 21 conference proceedings, and has delivered 180 conference presentations, 83 posters, and 80 invited talks at top universities, institutes, and companies worldwide. His work has resulted in 13 patents/patent applications and has placed him among the top 1% of authors in Royal Society of Chemistry journals by citations in 2020. As of March 2025, his research has received 25,991 citations with an h-index of 69, reflecting the high impact of his work globally.He is the Founder and Advisor of Ions Storage Systems, Maryland, USA (2012–present) and Founder and Director of Superionics, Calgary, Canada (2021–present), translating cutting-edge research into commercial energy storage technologies. His research highlights include optimizing lithium nucleation overpotentials in garnet-based hybrid solid-state batteries, developing doped sodium gadolinium silicate ceramics for fast Na⁺ conduction, and enhancing electrocatalysts for lithium–sulfur batteries.Prof. Thangadurai collaborates extensively with leading international researchers and institutions, including the University of Calgary, University of Maryland, University of St Andrews, University of Kiel, Yale University, and ANSTO, advancing cross-disciplinary solutions in energy materials. Beyond his scientific contributions, he mentors emerging scientists and actively promotes innovation that addresses global energy challenges. His work has significant societal impact, enabling safer, high-performance, and sustainable energy storage solutions critical for electric mobility, grid storage, and renewable energy integration.

Profiles: Google Scholar | ORCID | Scopus

Featured Publications

1. Murugan, R., Thangadurai, V., & Weppner, W. (2007). Fast lithium ion conduction in garnet-type Li₇La₃Zr₂O₁₂. Angewandte Chemie International Edition, 46(41), 7778–7781.
Cited By : 3691

2.Han, X., Gong, Y., Fu, K., He, X., Hitz, G. T., Dai, J., Pearse, A., Liu, B., Wang, H., … Thangadurai, V. (2017). Negating interfacial impedance in garnet-based solid-state Li metal batteries. Nature Materials, 16(5), 572–579. Cited By : 2088

3.Thangadurai, V., Narayanan, S., & Pinzaru, D. (2014). Garnet-type solid-state fast Li ion conductors for Li batteries: Critical review. Chemical Society Reviews, 43(13), 4714–4727. Cited By : 1712

4.Pal, B., Yang, S., Ramesh, S., Thangadurai, V., & Jose, R. (2019). Electrolyte selection for supercapacitive devices: A critical review. Nanoscale Advances, 1(10), 3807–3835.
Cited By : 1229

5.Wang, C., Fu, K., Kammampata, S. P., McOwen, D. W., Samson, A. J., Zhang, L., … Thangadurai, V. (2020). Garnet-type solid-state electrolytes: Materials, interfaces, and batteries. Chemical Reviews, 120(10), 4257–4300. Cited By : 1130

Prof. Dr. Venkataraman Thangadurai  pioneering research in solid-state chemistry and advanced battery technologies drives global innovation in energy storage, enabling safer, high-performance, and sustainable batteries that power electric mobility, renewable energy integration, and next-generation clean technologies.