Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

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Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

Associate Professor | Shanghai University | China

Prof. Mengmeng Liao is an accomplished researcher in artificial intelligence, computer vision, pattern recognition, and image processing, with a strong record of contributions to both foundational and applied aspects of visual computing. His work focuses on developing robust algorithms for face recognition, multi-resolution modeling, adaptive subspace learning, and representation learning, addressing complex challenges in real-world environments such as noise interference, limited samples, and multi-pose variation. He has authored more than 20 SCI/EI-indexed research papers, including publications in leading international journals such as Information Sciences, Neurocomputing, Expert Systems with Applications, Electronics, and IEEE Signal Processing Letters. His research impact is reflected in Scopus metrics, with 170 citations across 159 citing documents and an h-index of 6, alongside a growing presence on Google Scholar. Prof. Liao has also contributed to several major national research initiatives, securing competitive funding from programs such as the National Natural Science Foundation and the Postdoctoral Innovative Talent Support Program. His active engagement with the global academic community includes serving as a technical committee member, session chair, and program chair for numerous international conferences. Through his interdisciplinary approach and sustained research output, Prof. Liao continues to advance the field of artificial intelligence, particularly in intelligent visual perception, pattern learning, and computational recognition systems.

Publication Profile

Scopus

Publications

Fan, X., Liao, M., Chen, L., & Hu, J. (2023). Few-shot learning for multi-POSE face recognition via hypergraph de-deflection and multi-task collaborative optimization. Electronics.

Liao, M., Fan, X., Li, Y., & Gao, M. (2023). Noise-related face image recognition based on double dictionary transform learning. Information Sciences.

Fan, X., Liao, M., Xue, J., Wu, H., Jin, L., Zhao, J., & Zhu, L. (2023). Joint coupled representation and homogeneous reconstruction for multi-resolution small sample face recognition. Neurocomputing.

Liao, M., Li, Y., & Gao, M. (2022). Graph-based adaptive and discriminative subspace learning for face image clustering. Expert Systems with Applications.

Jiang, W., Li, Y., Liao, M., & Wang, S. (2021). An improved LPI radar waveform recognition framework with LDC-Unet and SSR-Loss. IEEE Signal Processing Letters.

 

Ms. Ifza Shad | Computer Vision | Research Excellence Award

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Ms. Ifza Shad | Computer Vision | Research Excellence Award

University of Central Punjab | Pakistan

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

Profile

ORCID

Featured Publications

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

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

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

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

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

Professor | Shaoyang University | China

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

Profile

Scopus | ORCID | Google Scholar

Featured Publications 

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

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

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

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

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

Yinlei Cheng | Computer Vision | Best Researcher Award

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Dr. Yinlei Cheng | Computer Vision | Best Researcher Award

Beijing Institute Of Fashion Technology | China

Dr. Yinlei Cheng is a dedicated postgraduate researcher at the Beijing Institute of Fashion Technology, specializing in artificial intelligence and innovative design. With a strong academic foundation in engineering and computing, he has developed expertise in deep learning, computer vision, and intelligent image processing. His research journey is marked by active involvement in collaborative projects bridging academia and industry, where he has focused on real-world challenges such as intelligent fabric recognition and fault diagnosis systems. Driven by a passion for research and innovation, he continues to explore advanced computational methods that contribute to both theoretical understanding and practical applications.

Publication Profile

Scopus

Education Background

Dr. Yinlei Cheng completed his undergraduate engineering studies at Shandong Jiaotong University, where he established a strong base in technology and problem-solving. He is currently pursuing a master’s degree at the School of Liberal Arts and Sciences, Beijing Institute of Fashion Technology, advancing his academic career with a focus on artificial intelligence applications. His educational path highlights a consistent pursuit of excellence, blending technical knowledge with practical applications in computer vision and image processing. Through this background, he has been able to integrate academic learning with innovative research contributions, strengthening his expertise in both theory and practice.

Professional Experience

Dr. Yinlei Cheng has been actively engaged in research-driven projects with direct industry relevance, showcasing his ability to apply cutting-edge methods to solve complex problems. His work on the intelligent fabric piece grasping system demonstrated his skill in combining deep learning and machine vision for non-rigid object recognition and automation. He also contributed to developing a portable fault diagnosis software system designed to provide real-time monitoring and predictive analysis of industrial equipment. These experiences reflect his growing professional maturity and highlight his potential to bridge academic research with practical industry solutions, ensuring his contributions have both scientific and applied value.

Awards and Honors

While Dr. Yinlei Cheng is still at an early stage in his research career, he has already achieved recognition through his publication in a peer-reviewed international journal indexed in high-ranking databases. His academic contributions, particularly in advancing activation functions for convolutional neural networks, have been cited by other researchers, reflecting the growing impact of his work. His dedication to refining theoretical insights and combining them with rigorous experimental validation has positioned him as a promising researcher. Although formal awards may not yet fully represent his contributions, his publication record and involvement in impactful projects underline his academic excellence.

Research Focus

The central focus of Dr. Yinlei Cheng’s research lies in computer vision, deep learning, and image processing, with a particular interest in designing intelligent systems for real-world applications. His work explores innovative activation functions to enhance the performance of convolutional neural networks, contributing both theoretical advancements and practical improvements. He also applies these concepts to industrial applications, such as automation in flexible manufacturing and predictive fault detection systems. By balancing theoretical depth with practical deployment, his research adds value to both academia and industry. His ongoing efforts aim to extend these methodologies to more advanced architectures and transformative technologies.

Publication Notes

Title: A Periodic Mapping Activation Function: Mathematical Properties and Application in Convolutional Neural Networks
Published Year: 2025
Citation: 1

Conclusion

Dr. Yinlei Cheng’s academic journey reflects a balance of solid educational grounding, active participation in significant projects, and meaningful contributions to the field of artificial intelligence. His work demonstrates the ability to translate theoretical research into applied solutions that address complex industry challenges. With an expanding publication record and growing recognition, he shows strong potential to emerge as a leading researcher in computer vision and deep learning. His commitment to rigorous research, clarity in academic writing, and focus on future innovations position him as a deserving candidate for recognition in the Best Researcher Award category.

Mr. Ayush Roy | Computer Vision | Young Researcher Award

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Mr. Ayush Roy | Computer Vision | Young Researcher Award

PhD, University at Buffalo, United States

Ayush Roy is an emerging researcher and innovator in the field of Electrical Engineering with a deep interest in AI, computer vision, and biomedical image analysis. Currently pursuing his B.E. at Jadavpur University, he has demonstrated exceptional potential through interdisciplinary research, AI-driven solutions, and impactful contributions to both academia and real-world applications. With multiple international publications and recognitions, Ayush is a dynamic force in the intersection of deep learning, signal processing, and intelligent systems.

Publication Profile

Google Scholar

🎓 Education Background

Ayush Roy is a final-year undergraduate student at Jadavpur University, West Bengal, India, enrolled in the Bachelor of Engineering (Electrical) program with an SGPA of 8.1/10 (2020–2024). He completed his schooling from Bhartiya Vidya Bhavan, West Bengal under the CBSE board, scoring 90.6% in Class 12 and a perfect CGPA of 10 in Class 10.

💼 Professional Experience

Ayush’s research journey began at Jadavpur University, working under renowned professors in Audio Signal Processing, Reinforcement Learning, and Image Segmentation. As a research intern at the Indian Statistical Institute, he contributed to dataset development and text detection models. He furthered his research as an intern at the University of Malaya on transformer-based networks and at IISc Bangalore on CLIP for image quality assessment. His work integrates deep learning models like YOLO, Swin Transformer, UNet, and CLIP with novel architectures and real-world applications.

🏆 Awards and Honors

Ayush has earned several accolades such as the Most Innovative Solution award at Hack-a-Web by NIT Bhopal (2021), 3rd Prize at FrostHack, IIT Mandi (2022), Top 10 in Cloud Community Hackday by GDG Cloud, and became a Finalist in both the IEEE R10 Robotics Competition and 404 Resolved hackathon at IIT Delhi.

🔬 Research Focus

His primary research areas include computer vision, medical image segmentation, scene text detection, and real-time AI systems. He is especially focused on lightweight models, attention mechanisms, domain adaptation, and hybrid approaches combining deep learning and signal processing. He has created multiple datasets for benchmarking including those for drone license plate detection, underwater text, water meter digit recognition, and circuit component recognition.

📌 Conclusion

Ayush Roy stands as a committed and creative researcher, blending electrical engineering fundamentals with cutting-edge AI methodologies. His work not only adds value to academic literature but also paves the way for practical, socially impactful AI systems. With an impressive early-career portfolio, Ayush continues to show immense promise for future contributions to science and technology.

📚 Top Publication Notes 

AWGUNet: Attention-aided Wavelet Guided U-net for nuclei segmentation in histopathology images

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 2 articles (Google Scholar)

A Wavelet Guided Attention Module for Skin Cancer Classification

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 1 article (Google Scholar)

A New Lightweight Attention-based Model for Emotion Recognition Using Distorted Social Media Images

Year: 2023

Journal/Conference: ACPR 2023

Cited By: 3 articles

Fourier Feature-based CBAM and Vision Transformer for Text Detection in Drone Images

Year: 2023

Conference: ICDAR WML 2023

Cited By: 1 article

A Lightweight Script Independent Scene Text Style Transfer Network

Year: 2024

Journal: International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)

Cited By: 1 article

Identification and Classification of Human Mental Stress using Physiological Data

Year: 2022

Conference: IEEE CATCON 2022

Cited By: 4 articles

Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 2 articles

An Attention-based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 1 article

DAU-Net: Dual Attention-aided U-Net for Segmenting Tumor Region in Breast Ultrasound Images

Year: 2023

Journal: PLOS ONE

Cited By: 6 articles

 

 

Dr. Aiai Wang | Machine Learning | Best Researcher Award

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Dr. Aiai Wang | Machine Learning | Best Researcher Award

Doctoral student, University of Science and Technology Beijing, China

Ai-Ai Wang is a passionate and dedicated young researcher born in March 1998 in Langfang, Hebei Province, China. A proud member of the Communist Party of China (CPC), she is currently based at the University of Science and Technology Beijing (USTB), where she serves as the Secretary of the 16th Party Branch, 4 Zhaizhai. With a solid academic foundation in mining and civil engineering, Ai-Ai has excelled in both academic and research spheres, contributing significantly to digital and intelligent mining technologies. Her work emphasizes physical dynamics in tailings sand cementation and filling, showing strong potential for innovation in sustainable mining practices.

Publication Profile

Scopus

🎓Education Background:

Ai-Ai Wang completed her Bachelor of Science in Mining Engineering from North China University of Science and Technology in 2021. She further pursued her Master’s degree in Civil Engineering at the University of Science and Technology Beijing (2021.09–2024.06), affiliated with the School of Civil and Resource Engineering.

🛠️Professional Experience:

Alongside her academic journey, Ai-Ai has undertaken significant responsibilities, currently serving as Secretary of the Party Branch at USTB. Her leadership extends beyond administration into collaborative research projects, software development, and patent contributions under renowned mentors such as Prof. Cao Shuai. She has played vital roles in developing intelligent systems for mining operations, reinforcing her multidisciplinary strengths.

🏅Awards and Honors:

Ai-Ai Wang has been recognized extensively for her academic and research excellence. Notable accolades include the “Top Ten Academic Stars” at USTB (2023), a National Scholarship for Master’s Degree Students (2022), the prestigious Taishan Iron and Steel Scholarship (2023), and multiple First-Class Academic Scholarships from USTB. She was twice named an Outstanding Three-Good Graduate Student and honored by her school as an outstanding individual. Moreover, she has received scientific awards such as the First Prize from the China Gold Association and the Second Prize from the China Nonferrous Metals Industry for her impactful contributions to green and safe mining.

🔬Research Focus:

Ai-Ai Wang’s research is rooted in advanced techniques of tailings sand cementation, intelligent filling systems, and digital mining. She explores the structural stability of backfills, application of nanomaterials, and CT-based 3D modeling of internal structures. Her work blends civil engineering, environmental safety, and digital innovation, aiming to enhance sustainability and efficiency in modern mining. She also contributes to cutting-edge software systems and patented technologies for mining design and operation support.

📝Conclusion:

Ai-Ai Wang stands out as a promising engineer and researcher whose academic achievements, professional dedication, and innovative research in intelligent mining set a high standard for future civil and mining engineers. Her trajectory reflects not just technical mastery but a deep commitment to sustainable and smart engineering solutions in the mining industry.

📚Top Publications with Details

Effect of height to diameter ratio on dynamic characteristics of cemented tailings backfills with fiber reinforcement through impact loading – Construction and Building Materials, 2022
Cited by: 26 articles
Influence of types and contents of nano cellulose materials as reinforcement on stability performance of cementitious tailings backfill – Construction and Building Materials, 2022
Cited by: 20 articles
Quantitative analysis of pore characteristics of nanocellulose reinforced cementitious tailings fills using 3D reconstruction of CT images – Journal of Materials Research and Technology, 2023
Cited by: 12 articles

 

Mr. Alvaro Martinez Ballester | Computer vision | Best Researcher Award

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Mr. Alvaro Martinez Ballester | Computer vision | Best Researcher Award

PhD researcher, Miguel Hernández University, Spain

Álvaro Martínez Ballester is a dedicated researcher in the fields of robotics, automation, and deep learning 🤖🎓. Currently working at Universidad Miguel Hernández de Elche as Research Personnel, he specializes in detecting and recognizing dynamic elements using 3D LiDAR and deep learning techniques. His work focuses on improving environmental mapping by eliminating moving objects, making maps more robust and reliable. With a strong academic background and hands-on experience, Álvaro is actively engaged in developing solutions that enhance the capabilities of mobile robotics and autonomous systems 🚀🔬.

Publication Profile

ORCID

🎓 Education

Álvaro holds a Bachelor’s degree in Electronic Engineering and Industrial Automation from Miguel Hernández University of Elche (2021) and a Master’s degree in Robotics from the same university (2022) 🎓🔍. His academic journey is marked by excellence, having achieved a perfect 10/10 score for both his Final Degree and Master’s projects. His research focused on EOG artifact removal in EEG signals and 3D LiDAR-based object detection using deep learning, demonstrating his strong analytical and technical skills 💡📊.

💼 Experience

With a solid foundation in research and industry applications, Álvaro has worked extensively with ROS modules, SLAM, and autonomous robotics 🤖. His previous roles at Universidad Miguel Hernández de Elche include Research Staff, Specialist Technician, and Intern, where he contributed to the development of mapping, control algorithms, and sensor integration for mobile robots 🚀. His expertise in deep learning for object detection and environmental mapping has been instrumental in advancing autonomous robotic navigation 🌍🤖.

🏆 Awards and Honors

Álvaro has demonstrated exceptional academic and research achievements, securing perfect scores (10/10) in his Bachelor’s and Master’s final projects 🏅📚. His dedication to scientific advancements in robotics and automation has positioned him as a promising researcher in the field. His research contributions are being recognized through his work on funded R&D projects and his involvement in cutting-edge LiDAR-based perception systems 🏆🔬.

🔬 Research Focus

Álvaro’s primary research revolves around deep learning for autonomous systems, LiDAR-based perception, and robotic mapping 🚀📡. He is particularly interested in developing advanced algorithms to filter out dynamic elements in real-time, ensuring more reliable environmental understanding for autonomous robots. His work integrates AI, robotics, and sensor fusion, paving the way for future advancements in self-driving technologies and intelligent automation 🤖💡.

🔍 Conclusion

Álvaro Martínez Ballester is a rising expert in robotics, automation, and AI-driven perception 🤖🚀. With a strong academic foundation, hands-on research experience, and innovative contributions to robotic vision and mapping, he is shaping the future of autonomous systems. His work not only advances robotic intelligence but also enhances real-world applications in autonomous navigation and environmental modeling 🌍🔬.

📚 Publication

A Method for the Calibration of a LiDAR and Fisheye Camera SystemApplied Sciences

2025-02-15 | journal-article