Naourez Benhadj | Deep Learning | Excellence in Research

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Prof. Naourez Benhadj | Deep Learning | Excellence in Research

Associate Professor | Ecole Nationale d’Ingénieurs de Sfax | Tunisian

Dr. Naourez Benhadj is a researcher at the Ecole Nationale d’Ingénieurs de Sfax (ENIS), Tunisia, specializing in electric machines, PMSM design, hybrid/electric vehicle energy management, and intelligent optimization techniques. With 32 scientific publications, 243 citations, and an h-index of 9, he has contributed significantly to fault detection, finite-element modeling, and advanced optimization algorithms, including recent work on transformer-based solar power prediction and PMSM design using chaotic PSO. Collaborating with over 30 international co-authors, his research supports sustainable mobility, smart energy systems, and high-efficiency electric transportation, fostering technological advancement and environmental impact on a global scale.

 

Citation Metrics (Scopus)

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Documents
32

h-index
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🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications


Comparison of fuel consumption and emissions of two hybrid electric vehicle configurations.

-International Conference on Sciences and Techniques of Automatic Control and Computer Engineering. (2018) Cited By: 4

Design simulation and realization of solar battery charge controller using Arduino Uno..

-International Conference on Sciences and Techniques of Automatic Control and Computer Engineering . (2017) Cited By: 21

Torque ripple and harmonic density current study in induction motor: Two rotor slot shapes.

– International Review on Modelling and Simulations.(2007). Cited By: 5

Thermal modeling of permanent magnet motor with finite element method.

– International Conference on Sciences and Techniques of Automatic Control and Computer Engineering. (2014). Cited By: 5

Qi Lai | Medical Image Analysis | Women Researcher Award

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Dr. Qi Lai | Medical Image Analysis | Women Researcher Award

Assistant Professor | Shenzhen Institutes of Advanced Technology | China

Dr. Qi Lai is a researcher at the Shenzhen Institutes of Advanced Technology, China, specializing in weakly supervised learning, medical image analysis, and multi-instance learning. He has authored 11 peer-reviewed publications, receiving over 52 citations with an h-index of 5. His work spans deep learning for pathology, object detection, semantic segmentation, and medical image restoration, often in collaboration with international teams across leading institutions. Notable contributions include interactive and hybrid MIL frameworks and contrastive learning methods that enhance diagnostic precision. His research advances reliable AI-driven clinical decision support, contributing to improved healthcare technologies and societal well-being.

 

Citation Metrics (Scopus)

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Citations
52

Documents
11

h-index
5

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Featured Publications


Fast broad multiview multi-instance multilabel learning (FBM3L) with viewwise intercorrelation.

– IEEE Transactions on Neural Networks and Learning Systems (2024). Cited By: 5 

Interactive multiple instance learning network for whole slide image analysis.

– Expert Systems with Applications. (2026). Cited By:  1

Joint discriminative latent subspace learning for image classification

– IEEE Transactions on Circuits and Systems for Video Technology. (2022). Cited By:: 15

Mueen Uddin | Medical Image Analysis | Research Excellence Award

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Prof. Dr. Mueen Uddin | Medical Image Analysis | Research Excellence Award

Professor | University Of Doha For Science and Technology | Qatar

Dr. Mueen Uddin is an Associate Professor of Cybersecurity and Data Sciences at the University of Doha for Science & Technology (UDST), Qatar. He is an internationally recognized researcher whose work bridges cybersecurity, blockchain technologies data science artificial intelligence, and healthcare security. His scholarly contributions reflect a strong commitment to advancing secure, intelligent, and sustainable digital systems across multidisciplinary domains.Dr. Uddin has authored over 192 peer-reviewed research publications in leading international journals and conferences, including IEEE Access, IEEE Network Renewable  Sustainable Energy Reviews, Sustainability and Health Informatics Journal. His research impact is evidenced by more than 7,404 citations an h-index of 43, and an i10-index of 99, underscoring the consistency quality and global relevance of his work. Several of his publications are widely cited benchmarks particularly in handwritten OCR systems medical image segmentation, blockchain for healthcare and digital twins energy-efficient data centers and IoT-enabled cybersecurity infrastructures.His research expertise spans Blockchain and Web 3.0, IoT and Cybersecurity Healthcare Security Metaverse technologies Deep Learning and Green IT systems. Dr. Uddin has played a pivotal role in advancing blockchain-based drug traceability solutions secure electronic health records intrusion detection systems and AI-driven healthcare analytics contributing directly to combating counterfeit drugs enhancing patient data security and improving diagnostic intelligence.Dr. Uddin actively collaborates with researchers across Asia Europe the Middle East and Africa fostering interdisciplinary and cross-border research initiatives. These collaborations have resulted in impactful studies addressing real-world challenges in smart cities sustainable development healthcare digitalization and intelligent network security.Beyond academia his work demonstrates strong societal and industrial relevance offering scalable secure solutions aligned with global priorities such as digital trust, sustainable computing, and resilient healthcare systems. Through high-impact research, academic leadership, and global collaboration Dr. Mueen Uddin continues to shape the future of cybersecurity and data-driven innovation worldwide.

Profiles: Scopus | ORCID | Googlescholar 

Featured Publications

1.Memon, J., Sami, M., Ahmed, R., & Uddin, M. (2020). Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR). IEEE Access, 8, 142642–142668. https://doi.org/10.1109/ACCESS.2020.3012542. Cited By : 730

2.Norouzi, A., Rahim, M. S. M., Altameem, A., Saba, T., Rad, A. E., Rehman, A., & Uddin, M. (2014). Medical image segmentation methods, algorithms, and applications. IETE Technical Review, 31(3), 199–213. https://doi.org/10.1080/02564602.2014.906861. Cited By : 440

3.Uddin, M., & Rahman, A. A. (2012). Energy efficiency and low carbon enabler green IT framework for data centers considering green metrics. Renewable and Sustainable Energy Reviews, 16(6), 4078–4094. https://doi.org/10.1016/j.rser.2012.03.002. Cited By : 318

4.Yaqoob, M. I. I., Salah, K., Uddin, M., Jayaraman, R., & Omar, M. (2020). Blockchain for digital twins: Recent advances and future research challenges. IEEE Network, 34(5), 290–298. https://doi.org/10.1109/MNET.2020.9225779. Cited By :  291

5.Uddin, M. (2021). Blockchain MedLedger: Hyperledger Fabric–enabled drug traceability system for counterfeit drugs in pharmaceutical industry. International Journal of Pharmaceutics, 597, 120235. https://doi.org/10.1016/j.ijpharm.2021.120235. Cited By : 273

Dr. Mueen Uddin’s research advances global innovation by integrating cybersecurity, blockchain, and AI to build secure, trustworthy, and sustainable digital ecosystems. His work delivers high-impact solutions for healthcare security, smart infrastructure, and data-intensive systems, translating scientific excellence into real-world societal and industrial benefits worldwide.

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

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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.

Tian Gao | Remote Sensing and Satellite Imagery Analysis | Research Excellence Award

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Dr. Tian Gao | Remote Sensing and Satellite Imagery Analysis | Research Excellence Award

The Information Engineering University | China

Dr. Tian Gao is a distinguished researcher in the field of remote sensing, specializing in multimodal image matching, Arctic sea ice motion analysis, and image registration for optical and SAR imagery. He completed his graduate studies at PLA Information Engineering University, Zhengzhou, China, focusing on geospatial information and advanced computational methods for Earth observation.Gao has authored 11 peer-reviewed publications, including in top-tier journals such as IEEE Sensors Journal, ISPRS Journal of Photogrammetry and Remote Sensing, and the International Journal of Applied Earth Observation and Geoinformation. His notable contributions include the development of SFA-Net, a SAM-guided focused attention network for multimodal remote sensing image matching, and innovative approaches to sharpened side phase fusion and self-similar adjacent self-convolutional feature registration. Gao’s work also encompasses keypoint-free feature tracking for Arctic sea ice motion retrieval, DEM super-resolution using attention-based and relative depth-guided methods, and GNSS-denied UAV geolocalization. These efforts have advanced both methodological innovation and practical applications in environmental monitoring, geospatial intelligence and disaster response.His research demonstrates extensive collaboration with domestic and international scholars, reflecting interdisciplinary engagement across remote sensing, UAV imaging, and geospatial data analysis. Gao’s publications have collectively received 51 citations, highlighting the growing impact of his work in the scientific community.Beyond methodological contributions Gao’s work has significant societal and environmental relevance enabling improved monitoring of polar ice dynamics, enhancing emergency response through UAV-assisted image stitching and supporting sustainable geospatial intelligence applications. With expertise spanning optical and SAR imagery multimodal data fusion and image registration, Tian Gao continues to contribute to cutting-edge research that bridges academic innovation with real-world solutions in Earth observation and remote sensing.

Profiles: ORCID | Scopus

Featured Publications

1.Wang, Y., Lan, C., Gao, T., Yao, F., & Mu, Z. (2025). Multimodal image matching using sharpened side phase fusion method. IEEE Sensors Journal.

2.Gao, T., Lan, C., Lv, L., Shi, Q., Huang, W., Wang, Y., & Mu, Z. (2025). Robust registration of multimodal remote sensing images using self-similar adjacent self-convolutional feature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

3.Gao, T., Lan, C., Zhou, C., Zhang, Y., Huang, W., Wang, L., & Wang, Y. (2025). Arctic sea ice motion retrieval from multisource SAR images using a keypoint-free feature tracking algorithm. ISPRS Journal of Photogrammetry and Remote Sensing.  Cited By: 1

4.Huang, W., Sun, Q., Guo, W., Xu, Q., Wen, B., Gao, T., & Yu, A. (2025). Multi-modal DEM super-resolution using relative depth: A new benchmark and beyond. International Journal of Applied Earth Observation and Geoinformation.

5.Gao, T., Lan, C., Huang, W., & Wang, S. (2025). SFA-Net: A SAM-guided focused attention network for multimodal remote sensing image matching. ISPRS Journal of Photogrammetry and Remote Sensing.

Tian Gao’s research advances remote sensing and multimodal image analysis, enabling precise monitoring of Arctic sea ice, GNSS-denied UAV navigation, and environmental changes. His work bridges scientific innovation with practical applications, supporting disaster response, geospatial intelligence, and sustainable environmental management globally.

Ahmed Elmekawy | Startups and Industry Applications | Research Excellence Award

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Dr. Ahmed Elmekawy | Startups and Industry Applications | Research Excellence Award

Researcher | Saint Petersburg State University | Egypt

Dr. Ahmed Hassan Abdelrahman Elmekawy is a researcher in Condensed Matter Physics, specializing in magnetic nanowires, FORC (First-Order Reversal Curve) analysis, micromagnetic modeling, and nanoscale magnetism. He is affiliated with JINR, St. Petersburg State University, and the Cyclotron Project at the Egyptian Atomic Energy Authority (EAEA). His work bridges theoretical modeling and advanced experimental techniques for understanding magnetic behavior in low-dimensional nanostructures.Dr. Elmekawy has authored 11 scientific publications, accumulating 71 citations, with an h-index of 4 and an i10-index of 3, reflecting his growing visibility and influence in nanomagnetism research. His contributions focus on unraveling magnetization dynamics internal magnetic interactions and structural property relationships in iron and Ni/Cu nanowire arrays which are foundational materials for next-generation spintronic devices magnetic sensors and energy-efficient data storage systems.Among his notable works his publication Magnetic Properties and FORC Analysis of Iron Nanowire Arrays stands as a highly cited study that advanced the interpretation of magnetic interactions through FORC techniques. His subsequent studies including “Magnetic Properties of Ordered Arrays of Iron Nanowires: The Impact of Length and Effect of Interactions and Non-uniform Magnetic States on Magnetization Reversal  further deepened scientific understanding of nanoscale magnetism and geometrical effects on magnetization reversal mechanisms.His recent publications in Nano-Structures & Nano-Objects and Journal of Magnetism and Magnetic Materials highlight significant advancements in correlating FORC measurements with micromagnetic simulations demonstrating compatibility between theoretical modeling and experimental observations. These studies provide new frameworks for evaluating internal magnetic interactions in segmented and non-segmented nanowire systems offering new tools for material optimization.In addition to nanomagnetism Dr. Elmekawy has contributed to nuclear physics particularly through work on proton and antiproton scattering from He using Glauber multiple scattering models. His interdisciplinary collaborations span Russia, Egypt, and Europe, showcasing strong international engagement.Dr. Elmekawy’s research contributes to societal and technological innovation by supporting the development of advanced magnetic materials crucial for secure communication systems biomedical imaging energy systems and miniaturized electronic components. His scientific trajectory reflects a commitment to precision collaboration and impactful discovery.

Profile: Googlescholar

Featured Publications

1.Elmekawy, A. H. A., Iashina, E. G., Dubitskiy, I. S., Sotnichuk, S. V., & Bozhev, I. V., et al. (2020). Magnetic properties and FORC analysis of iron nanowire arrays. Materials Today Communications, 25, 101609.  Cited By: 26

2.Elmekawy, A. H. A., Iashina, E., Dubitskiy, I., Sotnichuk, S., Bozhev, I., & Kozlov, D., et al. (2021). Magnetic properties of ordered arrays of iron nanowires: The impact of the length. Journal of Magnetism and Magnetic Materials, 532, 167951. Cited By: 20

3.Dubitskiy, I. S., Elmekawy, A. H. A., Iashina, E. G., Sotnichuk, S. V., & Napolskii, K. S., et al. (2021). Effect of interactions and non-uniform magnetic states on the magnetization reversal of iron nanowire arrays. Journal of Superconductivity and Novel Magnetism, 34(2), 539–549. Cited By: 16

4.Mistonov, A. A., Dubitskiy, I. S., Elmekawy, A. H. A., Iashina, E. G., & Sotnichuk, S. V., et al. (2021). Change in the direction of the easy magnetization axis of arrays of segmented Ni/Cu nanowires with increasing Ni segment length. Physics of the Solid State, 63(7), 1058–1064. Cited By: 7

5.Nabiyev, A. A., Mustafayev, I. I., Mehdiyeva, R. N., Nuriyev, M. A., & Andreev, E. V., et al. (2025). Post‐γ‐irradiation effects in nano-SiO2 particle reinforced high-density polyethylene composite films: Structure–property relationships, thermal stability, and degradation. Polymer Composites. Cited By: 1

Dr. Ahmed Elmekawy’s research in magnetic nanowires and FORC analysis advances fundamental understanding of nanoscale magnetism, enabling innovations in spintronics, high-density data storage, and energy-efficient magnetic devices. His interdisciplinary work bridges theory and experiment, contributing to technological development, materials science, and global scientific progress.

Jinxu Zhang | Document Image Analysis | Research Excellence Award

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Dr. Jinxu Zhang | Document Image Analysis | Research Excellence Award

Harbin Institute of Technology | China

Dr. Jinxu Zhang is a researcher at the Harbin Institute of Technology specializing in multimodal understanding, Document Visual Question Answering (DocVQA), and multimodal large language models. His work focuses on advancing key technologies for interpreting complex, multi-form, and multi-page documents, contributing significantly to the fields of document intelligence and machine reading systems.He has completed and continues to contribute to the National Natural Science Foundation of China (NSFC) project on Key Technologies of Multi-form Document VQA. His research outputs include six SCI/Scopus-indexed publications5 , with a total of 41 citations, an h-index of 2, and an i10-index of 2. His contributions appear in top-tier venues such as ACM Multimedia (CCF-A), EMNLP Findings (CCF-B), Information Fusion (SCI, IF 15.5), and IEEE Transactions on Learning Technologies. His notable works CREAM, DocRouter, DocAssistant, and DREAM introduce innovative solutions for hierarchical multimodal retrieval, prompt-guided vision transformers, mixture-of-experts connectors and robust reasoning strategies for document comprehension.Dr. Zhang’s patented work on an intelligent question-answering system for multi-form documents further extends his impact toward practical deployable intelligent document systems. His research achievements emphasize coarse-to-fine retrieval key-region reading step-wise reasoning and efficient multimodal fusion. He also incorporates Reinforcement Learning–based data enhancement and Chain-of-Thought (CoT) construction to improve model reasoning in multi-page document analysis.He actively collaborates with university researchers in multimodal understanding document analysis OCR and deep learning fostering interdisciplinary innovation. His work contributes to building reliable and generalizable document intelligence systems with broad societal applications including education digital governance business automation and large-scale knowledge management.Dr. Zhang continues to advance the frontier of intelligent document analysis through sustained research model innovation and high-impact scholarly contributions.

Profiles: Scopus |  Googlescholar

Featured Publications

1.Liu, M., Zhang, J., Nyagoga, L. M., & Liu, L. (2023). Student-AI question cocreation for enhancing reading comprehension. IEEE Transactions on Learning Technologies, 17, 815–826. Cited By: 28

2.Zhang, J., Yu, Y., & Zhang, Y. (2024). CREAM: Coarse-to-fine retrieval and multi-modal efficient tuning for document VQA. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 925–934). Cited By:  13

3.Zhang, J., Fan, Q., & Zhang, Y. (2025). DocAssistant: Integrating key-region reading and step-wise reasoning for robust document visual question answering. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 3496–3511).

4.Zhang, J., Fan, Q., Yu, Y., & Zhang, Y. (2025). DREAM: Integrating hierarchical multimodal retrieval with multi-page multimodal language model for documents VQA. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 4213–4221).

5.Zhang, J., & Zhang, Y. (2025). DocRouter: Prompt guided vision transformer and Mixture of Experts connector for document understanding. Information Fusion, 122, Article 103206.

Dr. Zhang’s research advances the global frontier of intelligent document understanding by enabling machines to accurately interpret complex, multi-page documents with human-level reasoning. His innovations in multimodal fusion, retrieval, and robust VQA architectures support breakthroughs in scientific research, digital governance, education, and automated knowledge management. Ultimately, his work drives the development of reliable, scalable, and socially beneficial AI systems that enhance information accessibility and decision-making worldwide.

Tchilabalo Bouyo | Microbiology | Research Impact Award

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Dr. Tchilabalo Bouyo | Microbiology | Research Impact Award

University of Lomé | Togo

Dr. Tchilabalo Bouyo is a Biological Engineer and early-career researcher specializing in molecular bacteriology, natural product chemistry, and antimicrobial resistance. With demonstrated experience in laboratory management and applied microbiological research, he has developed strong expertise in DNA extraction, PCR-based detection, and molecular characterization of pathogenic organisms. His analytical proficiency spans RStudio, SPSS, Excel, and GraphPad Prism, enabling robust statistical analysis data visualization and quality assurance in biomedical research. His background includes impactful roles at Centro Investigacion Biomedica and Infirmerie NDE Tokion Séminaire where he contributed to microbiological diagnostics antimicrobial resistance identification and laboratory quality compliance.Bouyo’s research output includes seven scientific documents consisting of journal articles experimental studies and preprints indexed in Scopus Clarivate and Crossref with 1 citation and an h-index of 1 reflecting a growing and internationally visible academic trajectory. A cornerstone of his scholarly contribution is the 2025 article Clarithromycin-resistant Helicobacter pylori in Africa: a systematic review and meta-analysis published in Antimicrobial Resistance  Infection Control. This influential work provides comprehensive evidence on antibiotic resistance trends across Africa contributing to global policy discussions and antimicrobial stewardship strategies.In parallel Bouyo has extensively investigated phytochemicals toxicity profiles and antimicrobial properties of medicinal plants widely used in traditional medicine in Togo and West Africa. His studies on species such as Pteleopsis suberosa Piliostigma thonningii Calotropis procera Anchomanes difformis and Tetrapleura tetraptera highlight the therapeutic potential of indigenous flora and support the scientific validation of ethnopharmacological practices. These works have appeared in journals such as Scientific Reports Journal of Applied Biotechnology and Chemical Science International Journal.Collaborating with multidisciplinary teams across Africa Asia and South America Bouyo contributes to global research networks in natural product drug discovery antimicrobial resistance surveillance and evidence-based traditional medicine. His work advances public health by bridging laboratory science cultural knowledge systems and modern biomedical innovation reinforcing his commitment to impactful and socially relevant global health research.

Profiles:  Scopus | ORCID

Featured Publications

1. Kossi, K., Komi, K. K., Sandrine, S. T., Bouyo, T., & Tchadjobo, T. (2025). Bioactive molecules isolated from Phyllanthus amarus Schum & Thonn, Caesalpinia bonduc (L.) Roxb, Momordica charantia Linn and Xylopia aethiopica (Dunal) A. Rich used in the Diabeto-Dolvo® recipe in Togo: A review. Chemical Science International Journal.

2. Dossouvi, K. M., Bouyo, T., Sognonnou, S., Ibadin, E. E., Lv, L.-C., Sambe Ba, B., Seck, A., Dossim, S., Sellera, F. P., Camara, M., et al. (2025). Clarithromycin-resistant Helicobacter pylori in Africa: A systematic review and meta-analysis. Antimicrobial Resistance and Infection Control.

3. Bouyo, T., Komi, K. K., Salifou, S. T., Pissang, P., Gbekley, H. E., Hoekou, Y., M’boumba, B. E., Kpatagnon, J. K., Bidjada, B., & Dossouvi, K. M., et al. (2025). Phytochemical and biological studies of the hydroethanolic extract of Pteleopsis suberosa and Piliostigma thonningii used in herbal medicine in Togo. Research Square.

4. Tchakondo, S., Boleti, N., Gamayizome, A. E., Bouyo, T., Gbekley, E. H., Tchacondo, T., & Karou, S. D. (2025). Phytochemical, toxicological, and antimicrobial evaluation of the hydroethanolic extract of Calotropis procera (Ait.) leaves used in traditional medicine in the Maritime Region of Togo: An experimental study. Research Square.

5. Bouyo, T., Komi, K. K., Salifou, S. T., Pissang, P., Gbekley, E. H., Hoekou, Y., M’boumba, B. E., Kpatagnon, J. K., Bidjada, B., Sossou, K., et al. (2025). In vitro phytochemical and biological evaluation of hydroethanolic extract of Anchomanes difformis in Togo. Scientific Reports.

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.

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

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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.