Zhang Zhenqian | Neuroscience | Best Researcher Award

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Mr. Zhang Zhenqian | Neuroscience | Best Researcher Award

University of Toyama | Japan

Mr. Zhang Zhenqian is a dedicated researcher whose work bridges artificial intelligence, machine learning, and meteorology, with an emphasis on developing advanced neural network models for predictive analytics. His recent publication, “RD2: Reconstructing the Residual Sequence via Under Decomposing and Dendritic Learning for Generalized Time Series Predictions,” featured in Neurocomputing (October 2025), showcases his innovative approach to enhancing time series forecasting accuracy through the integration of dendritic learning mechanisms and residual sequence reconstruction. Collaborating with Houtian He, Zhenyu Lei, Zihang Zhang, and Shangce Gao, Mr. Zhang contributes to advancing the computational intelligence field by addressing challenges in dynamic data modeling and predictive reliability. His research explores the intersection of data-driven modeling and environmental systems, offering valuable insights for improving real-world forecasting, particularly in meteorological and environmental applications. With a growing scholarly presence and contributions recognized through peer-reviewed international publications, Mr. Zhang exemplifies a new generation of researchers committed to interdisciplinary innovation. His work not only strengthens the theoretical foundations of artificial intelligence but also demonstrates its transformative potential in understanding and managing complex natural and engineered systems.

Profile : Orcid

Featured Publication

Zhang, Z., He, H., Lei, Z., Zhang, Z., & Gao, S. (2025). RD2: Reconstructing the residual sequence via under decomposing and dendritic learning for generalized time series predictions. Neurocomputing, 131867.

Sergio Manzetti | Mathematics | Best Researcher Award

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Dr. Sergio Manzetti | Mathematics | Best Researcher Award

Linnaeus University | France

Dr. Sergio Manzetti is a distinguished researcher whose interdisciplinary work bridges mathematics, quantum information theory, and nanotechnology. With an h-index of 26, 4,946 citations, and numerous scholarly documents, his research demonstrates both depth and global impact. His expertise spans mathematical analysis, Fourier analysis of partial differential equations (PDEs), quantum chemistry, computational systems, and nonlinear dynamics, contributing significantly to the understanding of quantum systems and wave phenomena. Dr. Manzetti’s academic foundation includes advanced degrees from Uppsala University, Linnaeus University, Queensland University of Technology, and Oslo University College, where he specialized in the mathematical and physical sciences. His professional experience is equally impressive—serving as an EU expert for Marie-Curie Fellowships, AI prompt reviewer at Mercor Intelligence, and researcher at Fjord-Research AS. He has co-authored influential publications in Analysis and Mathematical Physics, Advanced Theory and Simulations, and RSC Advances, addressing topics from eigenvalue problems of non-self-adjoint operators to supersymmetric wave equations and nanomaterial design. Skilled in Python, Mathematica, and MATLAB, Dr. Manzetti combines theoretical rigor with computational precision. His contributions to quantum information systems, rogue wave modeling, and nanotechnology continue to advance interdisciplinary research, positioning him as a leading figure in applied mathematics and theoretical chemistry.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Manzetti, S., & Khrennikov, A. (2025, September 28). Quantum and topological dynamics of GKSL equation in camel-like framework. Entropy.

Manzetti, S. (2025, September 22). Geometric formalism for quantum entanglement via B³ and S⁰ mappings. Preprint.

Manzetti, S., & Khrennikov, A. (2025, July 11). Quantum and topological dynamics of the GKSL equation in the camel-like framework. Preprint.

Kumar, R., Hiremath, K. R., & Manzetti, S. (2024, April). A primer on eigenvalue problems of non-self-adjoint operators. Analysis and Mathematical Physics.

Manzetti, S. (2021). Spectral properties of non-self adjoint operators: A review of the recent literature. Unpublished manuscript.

Kamerlin, N., Delcey, M. G., Manzetti, S., & van der Spoel, D. (2020, August 24). Toward a computational ecotoxicity assay. Journal of Chemical Information and Modeling.

Manzetti, S., & Trounev, A. (2020, January). Analytical solutions for a supersymmetric wave-equation for quasiparticles in a quantum system. Advanced Theory and Simulations.

Manzetti, S. (2020, January). Electromagnetic vorticity in a square-well crystal system described by a supersymmetric wave-equation. Advanced Theory and Simulations.

Ghisi, R., Vamerali, T., & Manzetti, S. (2019). Accumulation of perfluorinated alkyl substances (PFAS) in agricultural plants: A review. Environmental Research.

van der Spoel, D., Manzetti, S., Zhang, H., & Klamt, A. (2019, August 27). Prediction of partition coefficients of environmental toxins using computational chemistry methods. ACS Omega.

Manzetti, S., & Gabriel, J.-C. P. (2019, March 2). Methods for dispersing carbon nanotubes for nanotechnology applications: Liquid nanocrystals, suspensions, polyelectrolytes, colloids and organization control. International Nano Letters.

Manzetti, S., & Trounev, A. (2019, May). Supersymmetric Hamiltonian and vortex formation model in a quantum nonlinear system in an inhomogeneous electromagnetic field. Advanced Theory and Simulations.

Behzadi, H., Manzetti, S., Dargahi, M., Roonasi, P., & Khalilnia, Z. (2018). Application of calculated NMR parameters, aromaticity indices and wavefunction properties for evaluation of corrosion inhibition efficiency of pyrazine inhibitors. Journal of Molecular Structure.

Manzetti, S. (2018). Applied quantum physics for novel quantum computation approaches: An update. Computational Mathematics and Modeling.

Manzetti, S. (2018). Mathematical modeling of rogue waves, a review of conventional and emerging mathematical methods and solutions. Preprint.

Manzetti, S. (2018, November 8). Derivation and numerical analysis of an attenuation operator for non-relativistic waves. Scientific Reports.

Manzetti, S., & Lu, T. (2018, August 20). Addendum: Solvation energies of butylparaben, benzo[a]pyrene diol epoxide, perfluorooctanesulfonic acid, and DEHP in complex with DNA bases. Chemical Research in Toxicology.

Manzetti, S. (2018, June 20). Mathematical modeling of rogue waves: A survey of recent and emerging mathematical methods and solutions. Axioms.

Diogo Santiago | Computer Science | Best Researcher Award

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Mr. Diogo Santiago | Computer Science | Best Researcher Award

Oracle | Brazil

Mr. Diogo Santiago is a highly accomplished technology professional with extensive experience spanning software engineering, big data, and artificial intelligence. Beginning his career in 2009 as a software engineer developing major e-commerce platforms in Brazil, he transitioned into data engineering and science, mastering technologies like Hadoop, Spark, Hive, and Sqoop for large-scale data processing and migration. Since 2018, he has specialized in data science and AI, contributing to diverse projects in computer vision, anomaly detection, logistics optimization, and generative AI, including GAN and diffusion model applications for virtual try-on systems. As an AI Architect at Oracle for LATAM, he designs advanced AI architectures, supports clients with resource planning, and enhances model deployment efficiency through GPU optimization and large language model serving using vLLM and SGLang. His prior roles at Lambda3, Tivit, and Qintess involved developing ML models, data pipelines, and automation systems using cloud technologies such as GCP, AWS, and OCI. With multiple postgraduate qualifications in Big Data and Machine Learning for Finance, along with a Master’s in Medical Texture Imaging, he exemplifies innovation and leadership in merging AI research with scalable enterprise solutions.

Profile : Orcid

Featured Publication

Adorno, P. L. V., Jasenovski, I. M., Santiago, D. F. D. M., & Bergamasco, L. (2023, May 29). Automatic detection of people with reduced mobility using YOLOv5 and data reduction strategy. Conference paper.

 

Yaqin Wu | Computer Science | Excellence in Research Award

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Ms. Yaqin Wu | Computer Science | Excellence in Research Award

Shanxi Agricultural University | China

Ms. Yaqin Wu is an accomplished researcher and educator specializing in acoustic signal analysis, deep learning, and multimodal information fusion, with a research record reflecting 80 citations across 78 documents, 9 publications, and an h-index of 3. She holds a Master of Engineering in Electronic and Communication Engineering from Tianjin University and a Bachelor’s degree in Communication Engineering from Dalian Maritime University. Currently serving as a full-time faculty member at the School of Software, Shanxi Agricultural University, she teaches courses such as Speech Signal Processing, Natural Language Processing, and Human-Computer Interaction. Ms. Wu has led and contributed to several cutting-edge research projects, including pathological voice restoration, multimodal animal behavior monitoring, and AVS audio codec development. She has authored multiple SCI-indexed papers and holds several patents and software copyrights related to voice signal processing. Her technical proficiency spans Python, MATLAB, Linux systems, and MySQL databases. Notably, her master’s thesis earned the Outstanding Achievement Award of Engineering Master’s Practice from Tianjin University. Through her innovative contributions in signal processing and intelligent systems, Ms. Wu continues to advance the intersection of engineering and artificial intelligence research.

Profiles : Scopus | Orcid

Featured Publications

Zhang, J., Wu, Y., & Zhang, T. (2025). Fusing time-frequency heterogeneous features with cross-attention mechanism for pathological voice detection. Journal of Voice. Advance online publication.

Li, X., Wang, K., Chang, Y., Wu, Y., & Liu, J. (2025). Combining Kronecker-basis-representation tensor decomposition and total variational constraint for spectral computed tomography reconstruction. Photonics, 12(5), 492.

Victor R.L. Shen | Computer Science | Best Researcher Award

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Prof. Dr. Victor R.L. Shen | Computer Science | Best Researcher Award

National Taipei University | Taiwan

Prof. Dr. Victor R. L. Shen is a highly accomplished scholar and Professor Emeritus in the Department of Computer Science and Information Engineering at National Taipei University, Taiwan. With an extensive academic background, including a Ph.D. in Computer Science from National Taiwan University, he has dedicated decades to advancing research and education in artificial intelligence, Petri net theory, fuzzy logic, cryptography, e-learning systems, IoT, and intelligent computing. Over his distinguished career, he has published 78 documents that collectively received 840 citations across 696 sources, earning him an h-index of 15, reflecting both the depth and impact of his contributions. Beyond his prolific research, Prof. Shen has held prominent academic leadership positions, including Dean, Chairman, and CEO roles at National Taipei University and Ming Chi University of Technology, shaping academic programs and fostering innovation. His global recognition includes visiting professorships, membership in leading professional organizations such as IEEE, ACM, and IET, and numerous prestigious awards for teaching, research, and innovation. With sustained contributions in smart systems, advanced computing, and AI-driven education, Prof. Shen continues to influence the global academic community, leaving a legacy of excellence in both research and pedagogy.

Profiles : Scopus | Orcid

Featured Publications

Yang, C.-Y., Lin, Y.-N., Shen, V. R. L., Shen, F. H. C., & Lin, Y.-C. (2025). Petri net modeling and analysis of an IoT-enabled system for real-time monitoring of eggplants. Systems Engineering.

Yang, C.-Y., Lin, Y.-N., Shen, V. R. L., Shen, F. H. C., & Jheng, W.-S. (2025). A novel IoT-enabled system for real-time monitoring home appliances using Petri nets. IEEE Canadian Journal of Electrical and Computer Engineering.

Chang, J.-C., Chen, S.-A., & Shen, V. R. L. (2024). Smart bird identification system based on a hybrid approach: Petri nets, convolutional neural and deep residual networks. Multimedia Tools and Applications, 83(12), 34795–34823.

Yang, C.-Y., Lin, Y.-N., Shen, V. R. L., Tung, Y.-C., & Lin, J.-F. (2024). A novel IoT-enabled system for real-time face mask recognition based on Petri nets. IEEE Internet of Things Journal, 11(4), 6992–7001.

Yang, C.-Y., Lin, Y.-N., Wang, S.-K., Shen, V. R. L., & Lin, Y.-C. (2024). An edge computing system for fast image recognition based on convolutional neural network and Petri net model. Multimedia Tools and Applications, 83(5), 12849–12873.

Yang, C.-Y., Hwang, M.-S., Tseng, Y.-W., Yang, C.-C., & Shen, V. R. L. (2024). Advancing financial forecasts: Stock price prediction based on time series and machine learning techniques. Applied Artificial Intelligence, 38(1), 1–24.

Lin, Y.-N., Wang, S.-K., Chiou, G.-J., Yang, C.-Y., Shen, V. R. L., & Su, Z. Y. (2023). Development and verification of an IoT-enabled air quality monitoring system based on Petri nets. Wireless Personal Communications, 131(1), 63–87.*

 

Seyedeh Azadeh Fallah Mortezanejad | Mathematics | Best Researcher Award

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Dr. Seyedeh Azadeh Fallah Mortezanejad | Mathematics | Best Researcher Award

Jiangsu University | China

Author Profile

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Early Academic Pursuits

Dr. Seyedeh Azadeh Fallah Mortezanejad began her academic journey with a strong foundation in statistics, completing her undergraduate and postgraduate studies at Guilan University, Iran. Her master’s research on semi-parametric estimation of conditional copula reflected her interest in statistical theory and dependence structures. She advanced her academic training with doctoral research at Ferdowsi University of Mashhad, focusing on applications of entropy in statistical quality control, which laid the groundwork for her later interdisciplinary research.

Professional Endeavors

Following her doctoral studies, Dr. Mortezanejad pursued postdoctoral research at Jiangsu University in China, under the guidance of Professor Ruochen Wang. Supported by the National Natural Science Foundation of China, her work explored advanced applications of statistical inference in engineering systems. Her professional engagements span teaching, collaborative research, and presenting at international conferences, reflecting her role as both a researcher and an academic contributor.

Contributions and Research Focus

Her research lies at the intersection of statistics, data science, and engineering. She has significantly contributed to areas such as time series analysis, dependence data, deep learning, and statistical quality control. Her expertise in copula functions and entropy has enabled novel methods for addressing challenges in multivariate data analysis and control charts. More recently, her work integrates machine learning and physics-informed neural networks for solving complex problems in multivariate time series and image processing.

Accolades and Recognition

Dr. Mortezanejad’s scholarly contributions have been recognized through numerous publications in leading journals, including Entropy, Sankhya B, and Physica A. She has been invited to present her findings at international workshops in Germany, France, Vietnam, and Spain, underscoring her recognition in global research communities. Her role as a reviewer for reputed journals and conferences further reflects her professional standing in the field.

Impact and Influence

Through her interdisciplinary research, Dr. Mortezanejad has bridged the gap between theoretical statistics and practical applications in fields such as healthcare, engineering, and financial modeling. Her contributions to statistical quality control, machine learning applications, and Bayesian inference have influenced both academic discourse and applied research, making her work relevant across diverse scientific domains.

Legacy and Future Contributions

With her strong background in both theoretical and applied statistics, Dr. Mortezanejad is poised to continue advancing research in modern statistical methods, particularly in integrating entropy-based approaches with machine learning. Her future work is expected to focus on enhancing predictive analytics, developing robust statistical tools for big data, and contributing to sustainable innovations in engineering and healthcare.

Publications

Article: Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang, Ali Mohammad-Djafari
Journal: Entropy
Year: 2025

Article: Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal: Entropy
Year: 2024

Article: Variational Bayesian Approximation (VBA) with Exponential Families and Covariance Estimation
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal: Physical Sciences Forum
Year: 2023

Article: Variational Bayesian Approximation (VBA): A Comparison between Three Optimization Algorithms
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal/Conference: MaxEnt 2022 (Conference Proceedings)
Year: 2023

Article: Evaluation of Anti-lice Topical Lotion of Ozonated Olive Oil and Comparison of its Effect with Permethrin Shampoo
Authors: Omid Rajabi, Atoosa Haghighizadeh, Seyedeh Azadeh Fallah Mortezanejad, Saba Dadpour
Journal: Reviews on Recent Clinical Trials
Year: 2022

Conclusion

Dr. Seyedeh Azadeh Fallah Mortezanejad’s career reflects a rare blend of statistical rigor, innovative application, and international recognition. Her early commitment to statistical theory, coupled with her interdisciplinary contributions, has positioned her as a rising figure in applied statistics and data science. With her expanding research footprint, she is set to leave a lasting impact on statistical research and its applications in science, technology, and industry.

Hanlin Liu | Engineering | Research for community Impact Award

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Mr. Hanlin Liu | Engineering | Research for community Impact Award

Jilin Jianzhu University | China

Author profile

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Early Academic Pursuits

Mr. Hanlin Liu began his academic journey in the field of surveying and mapping engineering during his undergraduate studies at Jilin Jianzhu University. His dedication to precision and technical learning laid a strong foundation in geospatial sciences and civil engineering. With consistent performance and research-oriented thinking, he advanced to pursue a master’s degree in architecture and civil engineering at the same university. Under the guidance of his academic mentor, he cultivated a deep interest in remote sensing, machine learning, and environmental studies, setting the stage for his future research career.

Professional Endeavors

During his postgraduate years, Mr. Liu devoted himself to intensive laboratory work and field research. His professional endeavors included collaborative projects on soil analysis, wetland dynamics, mineral exploration, and fault diagnosis in mechanical systems. He demonstrated strong proficiency in scientific software, programming languages, and experimental design, which allowed him to develop advanced computational models and analytical frameworks. His role as an academic leader, serving as a class representative and editorial head, reflects his ability to balance research with organizational responsibilities.

Contributions and Research Focus

Mr. Liu’s research contributions span across environmental monitoring, mechanical fault diagnosis, and hyperspectral remote sensing. He explored the spatiotemporal dynamics of natural wetlands in Northeast China by integrating machine learning methods with optimization algorithms, offering new insights into ecological change drivers. His work on offshore wind turbine gearbox fault diagnosis proposed an interpretable, knowledge-driven framework that enriched mechanical reliability studies. Additionally, he advanced hyperspectral techniques for mineral alteration information extraction and developed innovative models to estimate soil heavy metal contents. These studies highlight his interdisciplinary focus combining artificial intelligence, geoscience, and environmental engineering.

Accolades and Recognition

Throughout his academic journey, Mr. Liu received multiple honors that reflect his excellence in research and innovation. He was awarded the National Scholarship and university-level first-class academic scholarships during his master’s program. His innovative projects earned recognition in provincial competitions, including awards in the “Internet+” Innovation and Entrepreneurship Contest, the “Challenge Cup,” and the Aerospace Knowledge Contest. During his undergraduate studies, he also won several distinctions in provincial surveying skill competitions, affirming his technical expertise and problem-solving ability.

Impact and Influence

Mr. Liu’s scholarly output includes multiple first-author and co-authored publications in high-impact journals indexed in SCI and EI. His research on wetlands, hyperspectral analysis, and mechanical fault diagnosis has been acknowledged in leading platforms, showcasing his ability to address both environmental and industrial challenges. Beyond publications, his patents for soil sampling and laser scanning devices demonstrate his commitment to translating research into practical technological solutions. His work not only contributes to scientific literature but also provides valuable methodologies for sustainable resource management and engineering applications.

Legacy and Future Contributions

Driven by a spirit of perseverance and innovation, Mr. Liu aspires to further his academic path through doctoral studies. His long-term vision is to refine computational methods for solving pressing environmental and engineering challenges. By integrating artificial intelligence with remote sensing and fault diagnosis systems, he seeks to contribute solutions with real-world impact. His dedication to teamwork, resilience under pressure, and scientific curiosity positions him as a researcher capable of leaving a lasting legacy in the interdisciplinary fields of environmental monitoring and intelligent engineering systems.

Publications

Article: Research on Abrasive Particle Target Detection and Feature Extraction for Marine Lubricating Oil
Authors: Chenzhao Bai, Jiaqi Ding, Hongpeng Zhang, Zhiwei Xu, Hanlin Liu, Wei Li, Guobin Li, Yi Wei, Jizhe Wang
Journal: Journal of Marine Science and Engineering
Year: 2024

Article: An axiomatic fuzzy set theory-based fault diagnosis approach for rolling bearings
Authors: Xin Wang, Hanlin Liu, Wankang Zhai, Hongpeng Zhang, Shuyao Zhang
Journal: Engineering Applications of Artificial Intelligence
Year: 2024

Article: An adversarial single-domain generalization network for fault diagnosis of wind turbine gearboxes
Authors: Xinran Wang, Chenyong Wang, Hanlin Liu, Cunyou Zhang, Zhenqiang Fu, Lin Ding, Chenzhao Bai, Hongpeng Zhang, Yi Wei
Journal: Journal of Marine Science and Engineering
Year: 2023

Article: Driving force analysis of natural wetland in Northeast plain based on SSA-XGBoost model
Authors: Hanlin Liu, Nan Lin, Honghong Zhang, Yongji Liu, Chenzhao Bai, Duo Sun, Jiali Feng
Journal: Sensors
Year: 2023

Article: Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image
Authors: Nan Lin, Hanlin Liu, Genjun Li, Menghong Wu, Delin Li, Ranzhe Jiang, Xuesong Yang
Journal: Open Geosciences
Year: 2022

Conclusion

Mr. Hanlin Liu is an emerging researcher whose academic pursuits blend civil engineering, remote sensing, and machine learning. His contributions span from ecological studies of wetlands to industrial fault diagnostics and soil heavy metal analysis, underpinned by strong technical skills and innovative methodologies. Recognized with scholarships, competition awards, and impactful publications, he has already established himself as a promising scholar. His future vision is centered on advancing scientific understanding and delivering practical solutions through rigorous doctoral research. With his blend of academic excellence, technical expertise, and research dedication, Mr. Liu represents the new generation of scholars poised to make meaningful contributions to science and society.

Vaggelis Lamprou | Computer Science | Best Researcher Award

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Mr. Vaggelis Lamprou | Computer Science | Best Researcher Award

National Technical University of Athens | Greece

Author Profile

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Early Academic Pursuits

Mr. Vaggelis Lamprou began his academic journey with a strong foundation in mathematics, earning his Bachelor’s degree from the National and Kapodistrian University of Athens, where he developed a deep interest in calculus, probability theory, and statistics. His passion for analytical reasoning and theoretical problem-solving led him to pursue a Master’s degree in Mathematics at the University of Bonn, Germany, where he focused on probability theory and its applications, culminating in a thesis on large deviations in mean field theory. This early academic phase not only honed his mathematical rigor but also laid the groundwork for his transition into the emerging domains of artificial intelligence and machine learning.

Professional Endeavors

Building upon his academic background, Mr. Lamprou advanced into roles that blended research with real-world applications. As a Data Analyst at Harbor Lab, he utilized statistical and computational tools to optimize platform usability and collaborated in developing innovative cost estimation tools for the maritime industry. His transition into machine learning engineering at Infili Technologies SA and later at the DSS Lab, EPU-NTUA, marked a shift toward high-impact AI-driven research and development, particularly within European-funded projects focusing on federated learning, generative AI, anomaly detection, and privacy-preserving technologies.

Contributions and Research Focus

Mr. Lamprou’s research is rooted in the intersection of mathematics, computer science, and artificial intelligence, with a strong emphasis on interpretable AI, deep learning, and probabilistic modeling. His work spans applications in medical imaging, cybersecurity, and large-scale distributed learning systems. In his Master’s thesis in Artificial Intelligence, he explored the evaluation of interpretability methods for deep learning models in medical imaging, underlining his dedication to developing transparent and trustworthy AI solutions. His contributions also extend to federated learning frameworks, enhancing data security and performance in next-generation communication networks.

Publications and Scholarly Engagement

His scholarly output reflects a commitment to both theoretical innovation and practical problem-solving. Notable works include a study on interpretability in deep learning for medical images published in Computer Methods and Programs in Biomedicine, and a comprehensive survey on federated learning for cybersecurity and trustworthiness in 5G and 6G networks in the IEEE Open Journal of the Communications Society. He actively participates in academic discourse, presenting at international conferences such as the International Conference on Information Intelligence Systems and Applications, further contributing to the global exchange of ideas in AI research.

Accolades and Recognition

Mr. Lamprou’s academic excellence is evident in his high academic distinctions throughout his studies, including top GPAs in his advanced degrees. His recognition extends beyond academic grades, with his selection to contribute to high-profile European R&D initiatives—a testament to his expertise and reliability in cutting-edge technological research. His invited participation in prestigious conferences and collaborations with leading research institutions reflects the respect he commands within the AI and machine learning community.

Impact and Influence

Through his research and professional activities, Mr. Lamprou has contributed to advancing AI methodologies in fields of societal importance, such as healthcare and cybersecurity. His work in interpretable AI has the potential to bridge the gap between complex machine learning models and human understanding, fostering trust in AI-assisted decision-making. In the realm of federated learning, his contributions support data sovereignty and privacy, addressing critical challenges in the deployment of AI at scale across sensitive domains.

Legacy and Future Contributions

As a PhD candidate at the National Technical University of Athens, Mr. Lamprou is poised to further deepen his contributions to the AI research landscape. His ongoing work aims to push the boundaries of interpretable and probabilistic AI models, with a vision to create transparent, reliable, and secure machine learning systems. His trajectory suggests a lasting influence on both the academic and industrial sectors, with the potential to inspire future researchers to prioritize ethical and explainable AI solutions.

Publications

Article: Federated Learning for Enhanced Cybersecurity and Trustworthiness in 5G and 6G Networks: A Comprehensive Survey
Authors: Afroditi Blika, Stefanos Palmos, George Doukas, Vangelis Lamprou, Sotiris Pelekis, Michael Kontoulis, Christos Ntanos, Dimitris Askounis
Journal: IEEE Open Journal of the Communications Society
Year: 2025

Article: On the trustworthiness of federated learning models for 5G network intrusion detection under heterogeneous data
Authors: Vangelis Lamprou, George Doukas, Christos Ntanos, Dimitris Askounis
Journal: Computer Networks
Year: 2025

Article: Data analytics for research on complex brain disorders
Authors: Michail Kontoulis, George Doukas, Theodosios Pountridis, Loukas Ilias, George Ladikos, Vaggelis Lamrpou, Kostantinos Alexakis, Dimitris Askounis, Christos Ntanos
Journal: Open Research Europe
Year: 2024

Article: On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness
Authors: Vangelis Lamprou, Athanasios Kallipolitis, Ilias Maglogiannis
Journal: Computer Methods and Programs in Biomedicine
Year: 2024

Article: Grad-CAM vs HiResCAM: A comparative study via quantitative evaluation metrics
Author: Vaggelis Lamprou
Institution: University of Piraeus
Year: 2023

Conclusion

With his blend of theoretical insight, technical skill, and a forward-looking research vision, Mr. Lamprou stands out as a promising researcher whose work is set to have a significant impact on the development of transparent and reliable AI technologies. His career embodies the bridge between rigorous academic inquiry and impactful, real-world AI solutions.

Ziang Liu | Engineering | Best Researcher Award

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Mr. Ziang Liu | Engineering | Best Researcher Award

Nanjing University | China

Author Profile

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Early Academic Pursuits

Mr. Ziang Liu began his academic journey with distinction at Tianjin University, where he earned his Bachelor of Science in Electronic Engineering. His strong foundation in engineering and mathematics laid the groundwork for advanced research and innovation. Continuing his academic trajectory, he pursued a Master of Science in Electronic Engineering at the prestigious Nanjing University, where he was recognized as an Outstanding Student and awarded the First-class Academic Scholarship.

Professional Endeavors

Ziang has accumulated valuable industry experience through impactful internships. At Meituan Shanghai, he served as an LLMs Evaluation Algorithm Intern, where he designed evaluation schemes and analyzed instruction-following capabilities across large language models such as Qwen, Doubao, ChatGPT 3.5/4, and Llama2-70B.  In another significant role at Alibaba DingTalk in Hangzhou, he worked on the back-end development of Chatmemo, an enterprise AI assistant. There, he implemented knowledge graph subgraph displays and integrated Retrieval-Augmented Generation (RAG), significantly boosting response speed and system performance.

Contributions and Research Focus

Mr. Liu’s core interests revolve around LLMs (Large Language Models), RAG (Retrieval-Augmented Generation), and knowledge graph technologies. He has contributed to the design and optimization of backend systems for intelligent applications in healthcare and enterprise settings. His work on deploying frameworks like Graph RAG and utilizing tools like Redis, MySQL, and Spring Boot has shown practical outcomes in real-world systems, particularly in performance optimization, load balancing, and cache management. His participation in the Nanjing University Intelligent Hospital Project resulted in a custom online medication purchasing system, complete with AI-powered Q&A capabilities and scalable backend infrastructure.

Accolades and Recognition

Ziang Liu’s academic excellence is evident through a remarkable series of accolades earned during both his undergraduate and postgraduate studies. He was honored as the Outstanding Student of Nanjing University in 2023 and received the First-class Academic Scholarship in 2022, recognizing his superior academic performance. His analytical and technical skills were demonstrated through competition achievements, including the Third Prize in the 19th Chinese Graduate Mathematical Modeling Competition (2022) and the Second Prize in the 18th Chinese Electronic Design Competition (2023). Earlier in his academic journey, he was named a Meritorious Winner in the Mathematical Contest in Modeling (MCM) in 2021 and was recognized as an Outstanding Graduate of Tianjin University in 2022. These accomplishments reflect his consistent dedication, innovation, and leadership in engineering and applied mathematics.

Impact and Influence

Ziang Liu’s work has made a tangible impact in both academia and industry. His efforts in improving instruction-following performance in LLMs and optimizing backend systems for enterprise AI applications have proven valuable for real-world implementation. His innovations in intelligent hospital systems demonstrate a commitment to applying advanced AI technologies to enhance societal well-being and operational efficiency.

Legacy and Future Contributions

Poised at the intersection of AI, backend engineering, and applied innovation, Mr. Ziang Liu is emerging as a key contributor to the next generation of AI infrastructure. His hands-on experience with cutting-edge technologies like gRPC, GraphRAG, JWT, and multi-threaded optimization positions him to drive future advancements in AI systems, enterprise platforms, and digital healthcare. With a strong academic record and robust technical expertise, he is well on his way to becoming a leading voice in intelligent systems development.

 

 

Publications

Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification

Authors: Ziang Liu, Kang Fan, Qin Gu, Yaduan Ruan
Journal: Bioengineering
Year: 2025

Studying Multi-Frequency Multilayer Brain Network via Deep Learning for EEG-Based Epilepsy Detection

Authors: Weidong Dang, Dongmei Lv, Linge Rui, Ziang Liu, Guanrong Chen, Zhongke Gao
Journal: IEEE Sensors Journal
Year: 2021

Tazebew Dires Kassie | Engineering | Editorial Board Member

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Mr. Tazebew Dires Kassie | Engineering | Editorial Board Member

Debre Markos University | Ethiopia

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🌱 Early Academic Pursuits

Mr. Tazebew Dires Kassie's journey into the realm of mechanical engineering began at Debre Markos University, where he earned his Bachelor’s degree in Mechanical Engineering in 2016 with an impressive GPA of 3.8/4.0. Fueling his passion for sustainability and innovation, he pursued a Master’s in Sustainable Energy Engineering from Bahir Dar University, graduating in 2021 with a perfect CGPA of 4.0/4.0. His thirst for continuous learning has since led him to expand his expertise through multiple Nano Degrees in Artificial Intelligence, Data Science, Android Development, and Programming Fundamentals from Udacity Online, and a Master Class in Online Teaching from Arizona State University.

🧑‍🏫 Professional Endeavors

Mr. Tazebew has been a dedicated academic and engineering professional since 2016, serving as a Lecturer at Debre Markos University. His leadership skills were further recognized when he was appointed Head of the Mechanical Engineering Department in 2024, where he now oversees academic programs, faculty development, research innovation, and strategic planning. Beyond academia, his early industry experience as a Maintenance Technician at Dashen Brewery S.C. in 2015 provided a strong foundation in preventive maintenance, machinery troubleshooting, and operational efficiency.

🔬 Contributions and Research Focus

Mr. Tazebew’s research contributions lie at the intersection of renewable energy systems, thermoelectric modules, and sustainable mechanical solutions. His notable studies include optimizing stove waste heat recovery using evaporative cooling, investigating CuO-water nanofluids in thermal systems, and analyzing performance parameters of direct evaporative coolers. His publications are featured in top-tier journals such as Springer – Energy Systems, Elsevier – Journal of Thermofluids, and Case Studies in Thermal Engineering. He has published five research papers, many of which are in press, and led two funded projects as a Principal Investigator.

🏅 Accolades and Recognition

Throughout his academic and professional career, Mr. Tazebew has received multiple awards that reflect his innovation and community dedication. He earned the Best BSc Thesis Award from Debre Markos University and received formal recognition for designing and manufacturing 6 wheelchairs with integrated toilet and shower services, and a shoeshine boy’s working shade, improving both accessibility and dignity. His outreach and project work have also earned him six certificates for community engagement, especially in aiding people with disabilities.

🌍 Impact and Influence

Mr. Tazebew’s work goes beyond the confines of classrooms and labs. His community-oriented engineering solutions highlight the practical application of sustainable technologies to address local needs. As a speaker and presenter at forums such as the Ethiopian Society of Mechanical Engineering and national research conferences, he continues to inspire peers and students alike with innovations in energy-efficient systems and design thinking. His teaching integrates real-world challenges with engineering fundamentals, empowering future professionals with critical problem-solving skills.

🔮 Legacy and Future Contributions

Mr. Tazebew Dires Kassie stands as a beacon of innovation and community-driven engineering in Ethiopia. With his growing expertise in AI, data science, and digital technologies, he is poised to bridge traditional mechanical engineering with cutting-edge interdisciplinary approaches. As he continues to lead his department and expand his research portfolio, his legacy is one of sustainable impact, academic excellence, and inclusive design. The future holds promise for more transformative projects and leadership in green energy solutions, smart technologies, and engineering education reform.

Publications

📄  Design and Fabrication of a Multi-Terrain Triphibian Quadcopter for Airborne, Terrestrial, and Aquatic Mobility
Author: Tazebew Dires Kassie
Journal: Journal of Intelligent & Robotic Systems
Year: 2025

📄  Experimental Investigation of Air Velocity, Water Flow Rate and Staging of Cooling Pad on the Performance of Direct Evaporative Coolers
Authors: Tazebew Dires Kassie, Yaregal Eneyew Bizuneh
Journal: International Journal of Air Conditioning and Refrigeration
Year: 2025

📄  Numerical Studies on Thermo-Hydraulic Performance of Solar Air Heater with Quarter-Circle Roughness Ribs
Authors: Tazebew Dires Kassie, Yaregal Eneyew, Amare Merfo
Journal: Results in Engineering
Year: 2025

📄  Numerical Investigation on Heat Transfer of CuO-Water Nano-Fluid in a Circular Pipe with Twisted Tape Inserts
Authors: Tazebew Dires Kassie, Yaregal Eneyew Bizuneh, Endalkew Berhie Gebresilassie, Atalay Enyew Bizuneh
Journal: International Journal of Thermofluids
Year: 2025

📄 Enhancing a Thermoelectric Power Generation System’s Efficiency from a Stove’s Waste Heat by Optimizing the Heat Sink Temperature Junction Using a Direct Evaporative Cooler
Authors: Bimrew Admasu, Tazebew Dires Kassie, Getu Alemayehu Melas, Hailemariam Mulugeta, S. Nagarajan
Journal: Energy Systems
Year: 2025