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

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

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0

Citations
381

Documents
41

h-index
11

🟦 Citations 🟥 Documents 🟩 h-index

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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

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

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       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

Riadh Harizi | Deep Learning For Computer Vision | Research Excellence Award

Dr. Riadh Harizi | Deep Learning For Computer Vision | Research Excellence Award

Teacher | Ecole Nationale d’Ingénieurs de Sfax | Tunisia

Dr. Riadh Harizi is a researcher at the École Nationale d’Ingénieurs de Sfax, Tunisia, with expertise in Machine Learning, Artificial Intelligence, Computer Vision, Deep Learning, and Data Science. He has authored 5 research outputs, receiving 33 citations across 25 citing documents and achieving an h-index of 3. His work spans scene text understanding, reinforcement learning, and AI-driven educational analytics, with publications in Applied Soft Computing, Multimedia Tools and Applications, and leading international conferences. He has collaborated with interdisciplinary teams and contributed an open Latin and Arabic scene character dataset to IEEE Dataport, supporting reproducible research and societal impact in education and intelligent visual systems.

 

Citation Metrics (Scopus)

80

60

40

20

0

Citations
33

Documents
5

h-index
3

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View ORCID Profile
     View Google Scholar Profile

Featured Publications


Deep-learning based end-to-end system for text reading in the wild.

-Multimedia Tools and Applications. (2022) Cited By: 10

SIFT-ResNet synergy for accurate scene word detection in complex scenarios.

– In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART) . (2024). Cited By: 3

Yanli Shi | Deep Learning for Computer Vision | Best Innovation Award

Dr. Yanli Shi | Deep Learning for Computer Vision | Best Innovation Award

Jilin University of Chemical Technology | China

Dr. Yanli Shi is a researcher at the Jilin Institute of Chemical Technology, Jilin, China, with recognized contributions in image processing, computer vision, and intelligent information technologies. As a first author, Dr. Shi has published nearly 20 high-quality SCI and EI-indexed journal articles, including three papers in JCR Zone 1 journals, reflecting strong research impact and international visibility. According to Scopus, Dr. Shi’s work has received 160 citations, with an h-index of 7, demonstrating consistent scholarly influence.Dr. Shi has led and successfully completed several competitive research projects, including one project funded by the Jilin Provincial Natural Science Foundation, one project under the “13th Five-Year Plan” Science and Technology Program of the Jilin Provincial Department of Education, and one vertical project supported by the Jilin Municipal Science and Technology Bureau, which also included the Outstanding Young Talent Cultivation Program. These projects have advanced both fundamental research and applied technological development.With a strong emphasis on technology transfer and practical innovation, Dr. Shi holds one national invention patent and has actively translated research outcomes into industrial solutions. Through extensive collaboration, Dr. Shi has participated in over 100 horizontal projects with Inner Mongolia University and local enterprises, generating more than 1.6 million yuan in research funding. These collaborations have addressed real-world technical challenges and promoted regional industrial and technological development.Dr. Shi’s recent publications in leading journals such as Pattern Recognition and Scientific Reports further highlight expertise in fine-grained visual classification, deep learning, and image super-resolution. Overall, Dr. Shi’s work demonstrates a balanced integration of academic excellence, cross-sector collaboration, and measurable societal and economic impact.

Profile: Scopus 

Featured Publications

1.Shi, Y., et al. (2025). Multi-scale adversarial diffusion network for image super-resolution. Scientific Reports.  Cited By: 1

2.Shi, Y., et al. (2025). LDH-ViT: Fine-grained visual classification through local concealment and feature selection. Pattern Recognition. Cited By : 1

Dr. Yanli Shi research advances state-of-the-art computer vision and image intelligence technologies, bridging fundamental algorithms with real-world industrial applications. Through high-impact publications, patented innovations, and extensive university–industry collaborations, the work delivers scalable solutions to practical technical challenges. This integration of scientific excellence and technology transfer contributes meaningfully to societal development and global innovation.

Abu Hanzala | Deep Learning for Computer Vision | Research Excellence Award

Mr. Abu Hanzala | Deep Learning for Computer Vision | Research Excellence Award

Research Assistant | Daffodil International University | Bangladesh

Mr. Abu Hanzala Daffodil International University, Dhaka, BangladeshHanzala, Abu is an emerging researcher specializing in artificial intelligence–driven medical image analysis, deep learning, and explainable healthcare systems. The researcher’s scholarly work focuses on developing robust hybrid and ensemble learning frameworks that integrate convolutional neural networks (CNNs), vision transformers (ViTs), graph neural networks (GNNs), transfer learning, self-supervised learning, and attention mechanisms for disease detection and classification.A key research achievement includes the publication of a peer-reviewed article in Array (2025) titled “A Hybrid Approach for Cervical Cancer Detection: Combining D-CNN, Transfer Learning, and Ensemble Models”, which demonstrates improved diagnostic accuracy using advanced ensemble strategies. In addition, the researcher has several manuscripts under peer review in high-impact international journals including Scientific Reports Neuroscience, IEEE Transactions on Medical Imaging, ACM Transactions on Computing for Healthcare, Discover Applied Science and Computers & Education: Artificial Intelligence. These studies address a wide range of clinically significant problems such as cervical, lung, and colorectal cancer, Alzheimer’s disease pneumonia neuromuscular disorders peripheral nerve disease and cerebral cortex pathology.The researcher has authored 5 scholarly documents receiving 5 citations, and currently holds an h-index of 2, reflecting a growing academic impact within the medical AI research community. International visibility is further strengthened through a peer-reviewed IEEE conference paper and an invited oral presentation at the 15th International Conference on Computing Communication and Networking Technologies (ICCCNT 2024).Research collaborations span multidisciplinary teams involving computer scientists biomedical engineers and healthcare researchers. The societal impact of this work lies in advancing early disease detection reliable clinical decision support and explainable AI models contributing to scalable trustworthy and globally relevant healthcare technologies.

Profiles: Scopus | ResearchGate

Featured Publication

1. Hanzala, A., Akter, T., & Rahman, M. S. (2025). A hybrid approach for cervical cancer detection: Combining D-CNN, transfer learning, and ensemble models. Cited By : 3

Mr. Abu Hanzala research advances global healthcare innovation by integrating reliable, explainable artificial intelligence with medical imaging to enable early disease detection and data-driven clinical decision support. This work bridges scientific rigor and real-world applicability, contributing to scalable, trustworthy AI solutions with meaningful societal and clinical impact.

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

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.

Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Dr. Zhongyuan Liu | Computer Vision | Best Researcher Award

Dr. Zhongyuan Liu | Computer Vision | Best Researcher Award

Doctorate at Beijing University of Technology, China

👨‍🎓 Profiles

Scopus

YOLO-TBD: Tea Bud Detection with Triple-Branch Attention Mechanism and Self-Correction

  • Author: Z. Liu, L. Zhuo, C. Dong, J. Li
    Journal: Industrial Crops and Products
    Year: 2025

Assoc Prof Dr. Qi Jia | Object Detection and Recognition | Best Researcher Award

Publications

Temporal refinement and multi-grained matching for moment retrieval and highlight detection

  • Authors: Zhu, C., Zhang, Y., Jia, Q., Wang, W., Liu, Y.
  • Journal: Multimedia Systems
  • Year: 2025

Bilevel progressive homography estimation via correlative region-focused transformer

  • Authors: Jia, Q., Feng, X., Zhang, W., Pu, N., Sebe, N.
  • Journal: Computer Vision and Image Understanding
  • Year: 2025

PMGNet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning

  • Authors: Liu, Y., Li, J., Zhang, Y., Pu, N., Sebe, N.
  • Journal: Computer Vision and Image Understanding
  • Year: 2024

WBNet: Weakly-supervised salient object detection via scribble and pseudo-background priors

  • Authors: Wang, Y., Wang, R., He, X., Jia, Q., Fan, X.
  • Journal: Pattern Recognition
  • Year: 2024

A rotation robust shape transformer for cartoon character recognition

  • Authors: Jia, Q., Chen, X., Wang, Y., Ling, H., Latecki, L.J.
  • Journal: Visual Computer
  • Year: 2024

Prof. Larbi Guezouli | Object Detection and Recognition | Best Researcher Award

Prof. Larbi Guezouli | Object Detection and Recognition | Best Researcher Award

Professor at Higher National School of Renewable Energies, Environment, Algeria

👨‍🎓 Profiles

Scopus

Orcid

Publications

SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions

  • Author: Bouafia, Y., Allili, M.S., Hebbache, L., Guezouli, L.
  • Journal: Signal Processing: Image Communication
  • Year: 2025

Human Detection in Clear and Hazy Weather Based on Transfer Learning With Improved INRIA Dataset Annotation

  • Author: Bouafia, Y., Guezouli, L., Lakhlef, H.
  • Journal: International Journal of Computing and Digital Systems
  • Year: 2024

Two-step text detection framework in natural scenes based on Pseudo-Zernike moments and CNN

  • Author: Larbi, G.
  • Journal: Multimedia Tools and Applications
  • Year: 2023

Human Detection in Surveillance Videos Based on Fine-Tuned MobileNetV2 for Effective Human Classification

  • Author: Bouafia, Y., Guezouli, L., Lakhlef, H.
  • Journal: Iranian Journal of Science and Technology – Transactions of Electrical Engineering
  • Year: 2022

Reading signboards for the visually impaired using Pseudo-Zernike Moments

  • Author: Guezouli, L.
  • Journal: Advances in Engineering Software
  • Year: 2022