Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

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Mr. Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

Master of Engineering | Hubei Normal University | China

Mr. Xuewen Zhou is a developing researcher in medical signal processing, medical image segmentation, and intelligent optimization algorithms, with growing contributions to the fields of biomedical engineering and computational intelligence. Affiliated with Hubei Normal University, his research focuses on designing advanced fractional-order and optimization-driven neural network models to enhance the analysis of physiological signals such as ECG and EEG as well as dermatological image segmentation. With 5 scientific publications, 4 citations, and an h-index of 1, Dr. Zhou is steadily establishing a strong academic presence.Dr. Zhou’s notable achievements include the publication of multiple SCI-indexed journal papers and active participation in leading international conferences. His recent SCI Q2 paper Adaptive Fractional Order Pulse Coupled Neural Networks with Multi-Scale Optimization for Skin Image Segmentation introduces an innovative segmentation framework integrating fractional order optimization with pulse coupled neural networks. The method employs a novel entropy–edge fitness function significantly improving accuracy in skin lesion delineation.Another key contribution is the SCI Q2 paper Improved Sparrow Search Based on Temporal Convolutional Network for ECG Classification where Dr. Zhou explores hybrid fractional order algorithms to optimize ECG recognition. His work rigorously analyzes the influence of positive and negative fractional orders on optimization stability offering valuable insights into next-generation fractional learning systems.In the EI indexed China Automation Congress Dr. Zhou proposed an ECG classification model combining spatial–channel attention networks with an improved RIME optimization algorithm enhancing hyperparameter tuning for complex biomedical patterns. He also contributed to neuromorphic computing through the ICNC  paper on FRMAdam iTransformer KAN presenting a fractional order momentum optimizer for EEG and ECG prediction.Dr. Zhou maintains strong collaborations with researchers including Jiejie Chen Ping Jiang Xinrui Zhang Zhiwei Xiao and Zhigang Zeng contributing to interdisciplinary advancements across medical AI fractional order theory and neural computation. His research demonstrates meaningful societal impact by improving early disease detection supporting intelligent diagnostic tools and advancing clinical decision making technologies on a global scale.

Profiles: Scopus | ORCID | ResearchGate

Featured Publications

1.Zhou, X., Chen, J., Jiang, P., Zhang, X., & Zeng, Z. (2026). Adaptive fractional-order pulse-coupled neural networks with multi-scale optimization for skin image segmentation. Biomedical Signal Processing and Control, (February 2026).

2.Zhou, X., Chen, J., Xiao, Z., Zhang, X., Jiang, P., & Zeng, Z. (2026). Improved sparrow search based on temporal convolutional network for ECG classification. Biomedical Signal Processing and Control, (February 2026).

3.Xiao, Z., Chen, J., Zhou, X., Wei, B., Jiang, P., & Zeng, Z. (2025). Monotonic convergence of adaptive Caputo fractional gradient descent for temporal convolutional networks. Neurocomputing, (December 2025).

4.Zhang, X., Chen, J., Zhou, X., & Jiang, P. (2024, December 13). FRMAdam-iTransformer KAN: A fractional order RMS momentum Adam optimized iTransformer with KAN for EEG and ECG prediction. In 2024 International Conference on Neuromorphic Computing (ICNC).

5.Zhou, X., Chen, J., Jiang, P., & Zhang, X. (2024, November 1). Electrocardiogram classification based on spatial-channel networks and optimization algorithms. In 2024 China Automation Congress (CAC).

Dr. Xuewen Zhou’s work advances science and society by developing fractional-order neural systems that significantly enhance the accuracy of biomedical signal and image analysis. His innovations support earlier disease detection, improved diagnostic reliability, and broader global access to intelligent healthcare technologies.

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.

 

Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

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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.

Profile: Google Scholar | Scopus | ORCID 

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.

Kaplan-Kaplan-Deep Learning for Computer Vision-Best Researcher Award

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Kaplan-Kaplan Deep Learning for Computer Vision-Best Researcher Award

Kocaeli University-Turkey

Author Profile

Early Academic Pursuits

Kaplan Kaplan's academic journey commenced at Kocaeli University, Turkey, where they pursued a Bachelor's in Mechatronic Engineering from 2007 to 2012. This foundation was followed by a Master's degree in Mechatronic Engineering from 2013 to 2015 and culminated in a Ph.D. in Mechatronic Engineering from Kocaeli University's Institute of Science and Technology in 2020.

Professional Endeavors

Transitioning into academia, Kaplan Kaplan undertook various roles at Kocaeli University, currently serving as an Assistant Professor in Software Engineering at the Faculty of Engineering since 2021. This role emphasizes their commitment to interdisciplinary engineering fields.

Contributions and Research Focus

With a research focus spanning algorithms, Artificial Intelligence, Computer Learning and Pattern Recognition, Software, and Biomedical Image Processing, Kaplan Kaplan's contributions are extensive and impactful. They've made significant strides in diverse areas, including:

  • Fault Diagnosis: Specializing in fault diagnosis, particularly in bearing faults, Kaplan Kaplan has developed novel approaches using deep learning models and pattern recognition methods to diagnose faults accurately.
  • Healthcare Applications: Their work in biomedical image processing extends to healthcare, contributing to brain tumor classification, thyroid nodule diagnosis, and spondyloarthritis detection through innovative machine learning algorithms applied to medical imaging.
  • Machine Learning and AI: They've also delved into the development and optimization of machine learning algorithms, exploring their applications in various domains, including sustainable balanced scorecards, control systems, and predictive models for different scenarios.

Accolades and Recognition

Kaplan Kaplan's extensive publication record and contributions to academic literature are reflected in a substantial number of peer-reviewed articles, book chapters, and proceedings across prestigious international conferences. Their metrics, with 74 publications and notable citation indices (159 in WoS and 420 in Scopus), underscore their impact and influence in the academic domain.

Impact and Influence

Their multidisciplinary approach to engineering and AI has contributed significantly to advancing fault diagnosis methodologies, medical imaging applications, and the optimization of machine learning algorithms. These contributions have the potential to influence various industries, particularly in fault diagnosis systems, healthcare, and predictive analytics.

Legacy and Future Contributions

Kaplan Kaplan's legacy lies in their pioneering research that merges engineering principles with cutting-edge AI methodologies. Their future contributions are likely to continue shaping fault diagnosis systems, medical imaging technologies, and the broader landscape of machine learning applications in diverse industries, leaving a lasting impact on academia and practical implementations.

Notable Publications