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

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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)

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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

Irenilza De Alencar Nääs | Object Detection and Recognition | Women Researcher Award

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Prof. Irenilza De Alencar Nääs | Object Detection and Recognition | Women Researcher Award

Professor | Universidade Paulista | Brazil

Prof. Irenilza de Alencar Nääs is a leading researcher at Universidade Paulista, São Paulo, Brazil, specializing in precision livestock farming, agricultural engineering, and AI-driven animal welfare assessment. She has authored over 339 peer-reviewed publications with more than 3,311 citations h-index 32, reflecting strong international impact and extensive collaboration with more than 400 co-authors worldwide. Her recent work integrates thermography, computer vision (YOLOv8), and machine learning to improve broiler welfare, postharvest quality, and occupational health in agri-food systems. Dr. Nääs’s research significantly advances sustainable agriculture and data-driven decision-making for global food security.

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3,311

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339

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Featured Publications


Princípios de conforto térmico na produção animal .

– Ícone Editora.. (1989). Cited By : 251

Infrared thermal image for assessing animal health and welfare.

-Journal of Animal Behaviour and Biometeorology. (2014). Cited By: 143

Impact of lameness on broiler well-being.

– Journal of Applied Poultry Research. (2009). Cited By: 116

Real time computer stress monitoring of piglets using vocalization analysis.

– Computers and Electronics in Agriculture. (2025). Cited By: 108

Nada Alzaben | Deep Learning for Computer Vision | Research Excellence Award

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Dr. Nada Alzaben | Deep Learning for Computer Vision | Research Excellence Award

Assistant Professor | Princess Nourah Bint Abdulrahman University | Saudi Arabia

Dr. Nada Alzaben is an Assistant Professor at Princess Nourah bint Abdulrahman University (PNU), Saudi Arabia, and a recipient of the Research Excellence Award. Her expertise spans networking, scheduling algorithms, IoT systems, reinforcement learning, deep learning, and remote sensing analytics. She has authored 28 peer-reviewed publications with 49 citations, an h-index of 4, and sustained scholarly impact since 2020. Her research integrates AI-driven optimization with real-world applications including phishing detection, SDN routing, UAV surveillance, landslide monitoring, smart agriculture, and marine pollution mapping. Through extensive international collaborations, Dr. Alzaben contributes to advancing sustainable digital infrastructures and intelligent societal systems.

Citation Metrics (Scopus)

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49

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Featured Publications


End-to-end routing in SDN controllers using max-flow min-cut route selection algorithm.

-In Proceedings of the 2021 23rd International Conference on Advanced Communication Technology (ICACT). (2021). Cited By: 10

The most promising scheduling algorithm to provide guaranteed QoS to all types of traffic in multiservice 4G wireless networks.

-In Proceedings of the 2012 Ninth International Joint Conference on Computer Science and Software Engineering (JCSSE). (2012). Cited By: 6

Steven Sheng-Uei Guan | Machine Learning for Computer Vision | Research Excellence Award

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Prof. Dr. Steven Sheng-Uei Guan | Machine Learning for Computer Vision | Research Excellence Award

Professor | Xi’an Jiaotong-Liverpool University | Australia

Prof. Dr. Steven Sheng Uei Guan is an accomplished researcher at Xi’an Jiaotong-Liverpool University, China, with a Scopus h-index of 25, over 244 publications, and more than 2,362 citations. His research expertise spans human–object interaction detection, graph neural networks, continual learning, human–robot interaction, blockchain-enabled data trading, and intelligent healthcare systems. Dr. Guan has collaborated with over 200 international co-authors, reflecting his strong global research network. His work contributes significantly to advancing artificial intelligence for real-world perception, secure data sharing, and socially beneficial intelligent systems, impacting domains such as robotics, medical informatics, and computational social systems.

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Featured Publications


Encyclopedia of information science and technology.

– IGI Global. (2018). Cited By : 454

Parameter estimation of photovoltaic models via cuckoo search.

-Parameter estimation of photovoltaic models via cuckoo search. (2013). Cited By: 303

An incremental approach to genetic-algorithms-based classification.

-Multimedia Tools and Applications. (2005). Cited By: 124

Investigation of neural networks for function approximation.

– Procedia Computer Science. (2013). Cited By: 111

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

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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)

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33

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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

Zeng Gao | Applications of Computer Vision | Research Excellence Award

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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)

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98

Documents
12

h-index
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Featured Publications


Visual tracking with levy flight grasshopper optimization algorithm.

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

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

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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.

Simy Baby | Applications of Computer Vision | Best Researcher Award

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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.

Vasuki | Deep Learning for Computer Vision | Women Researcher Award

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Dr. R. Vasuki | Deep Learning for Computer Vision | Women Researcher Award

Assistant Professor | Mannar Thirumalai Naicker College | India

Dr. R. Vasuki is an Assistant Professor in the Department of Artificial Intelligence at Mannar Thirumalai Naicker College, Madurai. She holds a Ph.D. in Computer Science from Karpagam Academy of Education, along with M.Phil, MCA, and BCA degrees from Bharathidasan University and Cauvery College for Women. She has over fourteen years of academic experience and previously served as an Assistant Professor at Annai Fathima College and as a Website Developer at LM Technologies, Chennai. Her research interests include biometrics, cryptography, database management systems, web development, and artificial intelligence. She has published several papers in reputed international journals and conferences such as IEEE, Springer, and Scopus-indexed publications, with notable work in biometric template protection, image encryption, and machine learning applications. Dr. Vasuki has organized and participated in numerous faculty development programs, workshops, and seminars, and has contributed as a reviewer for reputed journals. She received the first prize for a paper presentation from the Madurai Productivity Council and has authored a book titled Internet of Things along with a book chapter on conversational AI applications. Her research skills include data analysis, model optimization, and AI-driven system development, supported by certifications in deep learning, cybersecurity, and cloud computing. She actively mentors students in technical skill development and promotes innovation in higher education. Her research has received 1 citation by 3 documents with an h-index of 1.

Profile: Scopus

Featured Publications

1. Vasuki, R. (2024). Iris biometric template identification and recognition scheme using a novel parallel fused encoder.

 

Xinrong Hu | Object Detection and Recognition | Women Researcher Award

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Prof. Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Dean of Computer Science and Artificial Intelligence | Wuhan Textile University | China

Prof. Xinrong Hu is a distinguished researcher and academic leader in computer vision, natural language processing, virtual reality, and machine learning. She serves as Dean of the School of Computer and Artificial Intelligence at Wuhan Textile University and is a doctoral supervisor, leading an innovative research team at the Hubei Provincial Engineering Technology Research Center for Garment Informatization. She holds a Ph.D. and has extensive experience in guiding research projects, including over 30 funded initiatives, some with national and international significance. Her research interests focus on advancing artificial intelligence applications in real-world scenarios, combining theoretical innovation with practical solutions. She has authored more than 100 academic papers, edited six textbooks, translated a book, and holds 26 invention patents, demonstrating her strong research skills and contribution to knowledge dissemination. Prof. Hu has been recognized with multiple awards and honors, including provincial and ministerial-level scientific research awards, teaching achievement awards, and prestigious titles such as Hubei Provincial Distinguished Teacher and recipient of the Special Government Allowance from the State Council. Her professional engagement includes leadership in academic communities, mentorship of young researchers, and active participation in advancing the field of AI through both education and research initiatives. Her comprehensive expertise, innovative contributions, and dedication to fostering academic excellence make her a leading figure in her field. Her research impact is reflected in 1,044 citations, 209 documents, and an h-index of 16.

Profiles: Scopus | ResearchGate 

Featured Publications

  1. Hu, X., et al. (2025). CDPMF-DDA: Contrastive deep probabilistic matrix factorization for drug-disease association prediction. BMC Bioinformatics.

  2. Hu, X., et al. (2025). Source-free cross-modality medical image synthesis with diffusion priors. Journal of King Saud University – Computer and Information Sciences.

  3. Hu, X., et al. (2025). TADUFMA: Transformer-based adaptive denoising and unified feature modeling for multi-condition anomaly detection in computerized flat knitting machines. Measurement Science and Technology.

  4. Hu, X., et al. (2025). ViT-BF: Vision transformer with border-aware features for visual tracking. Visual Computer.

  5. Hu, X., et al. (2025). Adaptive debiasing learning for drug repositioning. Journal of Biomedical Informatics.