Mrs. Hanane ABBOU | Biomedical Engineering | Research Excellence Award

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Mrs. Hanane ABBOU | Biomedical Engineering | Research Excellence Award

Mrs. Hanane ABBOU | Mohammed VI university of Sciences and Health | Morocco

Mrs. Hanane ABBOU is a PhD Candidate and Research Assistant in Medical Biotechnology at Mohammed VI University of Sciences and Health and the Mohammed VI Center for Research and Innovation. Her research focuses on computational investigations of cannabinoid-based therapeutics targeting the endocannabinoid system, with emphasis on Cannabis and Moroccan genetic diversity. She holds an MSc in Medical Biotechnology and has professional experience in pharmacovigilance and scientific publishing. Her scholarly profile reflects 14 citations by 13 documents, 7 published documents, and an h-index of 2, with the h-index view disabled in preview mode, highlighting her growing research impact in computational pharmacology.

Citation Metrics (Scopus)

20

15

10

5

0

Citations
14

Documents
7

h-index
2

🟦 Citations    🟥 Documents    🟩 h-index


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Featured Publications

Prof. Dr. Chengli Sun | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Chengli Sun | Artificial Intelligence | Best Researcher Award

Prof. Dr. Chengli Sun | Guangzhou Maritime University | China

Prof. Dr. Chengli Sun is a distinguished scholar in the field of signal and information processing, widely recognized for his significant contributions to intelligent acoustic technology and next-generation speech systems.To date, his work has accumulated 336 citations across 303 documents, demonstrating the broad recognition and adoption of his research outcomes by both domestic and international peers. With 46 published documents spanning high-impact journals His research encompasses speech recognition, speech enhancement, acoustic scene analysis, and computer vision, with a strong focus on advancing human–machine voice interaction under complex and noisy environments. He has led a series of high-impact scientific projects, including major National Natural Science Foundation of China grants and key provincial and municipal initiatives, driving breakthroughs in generative adversarial network models, dual-diffusion speech enhancement, sustainable learning-oriented vehicle voice interaction, and speaker-specific keyword spotting. These research outcomes have enabled practical advancements in intelligent transportation, robotics, smart devices, and public safety. Alongside his research achievements, Prof. Dr. Chengli Sun plays an integral role in academic development and scientific service by contributing to expert review committees and supporting the progress of information processing and acoustics disciplines. He remains committed to high-level talent cultivation through leadership in first-class undergraduate teaching programs and the promotion of interdisciplinary innovation in artificial intelligence and signal processing. Collectively, his sustained research efforts, academic influence, and dedication to education position him as a leading figure shaping the future of intelligent voice technologies.

Profiles: Scopus | Orcid

Featured Publications

  • Sun, M., Sun, C., Zou, C., Zhang, J., & Xiang, D. (2025). Modeling of multi-electrode epicardial electrograms for conductivity estimation in atrial fibrillation. IEEE Access.

  • Li, J., Xiang, D., Li, C., Mao, S., Chen, Y., Sun, M., He, W., Deng, Y., & Sun, C. (2025, December 3). Learning student knowledge states from multi-view question–skill networks. Symmetry.

  • Leng, Y., Zhang, E., Zhuang, J., Shen, C., Sun, C., Yuan, Q., & Pan, J. (2025, October). A topic-specific representation learning framework for acoustic scene classification. Applied Soft Computing.

  • Rao, Z., Sun, C., Sun, J., Chen, F., Leng, Y., Sun, M., & Guo, Q. (2025, October 16). A new speech enhancement model based on residual denoising diffusion. Circuits, Systems, and Signal Processing.

  • Wan, M., Zhu, J., Sun, C., Yang, Z., Yin, J., & Yang, G. (2024). Tensor low-rank graph embedding and learning for one-step incomplete multi-view clustering. IEEE Transactions on Multimedia.

 

 

Mrs. Elzbieta Raus-Jarząbek | Biomedical Engineering | Research Excellence Award

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Mrs. Elzbieta Raus-Jarząbek | Biomedical Engineering | Research Excellence Award

Mrs. Elzbieta Raus-Jarząbek | AGH University of Krakow | Poland

Mrs. Elżbieta Raus-Jarząbek is a highly accomplished Software and Electronic Engineer and emerging biomedical researcher, with 5 scientific documents, 4 citations by 4 documents, and an h-index of 1, reflecting her growing academic impact alongside a strong engineering career. She combines expertise in electronics, telecommunications, biomedical signal processing, machine learning, and computer science. Her professional background includes significant experience at Motorola Solutions, where she worked in Unix and Windows virtualization, software deployment, installation package creation for Linux and Windows platforms, and automated testing, including web-based systems, while troubleshooting complex multi-environment infrastructures. She later contributed to Noble Systems Corporation in Kraków as a Software Engineer, developing cross-platform telecommunication software and SIP-based VoIP desktop applications using network programming techniques. Alongside industry roles, she gained broad hands-on experience through consulting and freelance work, designing sensor-based electronic interfaces and creating systems for data acquisition, processing, and interpretation. Currently, as a PhD candidate at AGH University of Science and Technology, she focuses on designing and validating wearable ECG devices and developing advanced ECG signal-processing and HRV-based cardiovascular risk prediction approaches using nonlinear analysis and machine learning. Her multidisciplinary expertise bridges electronics, software, and biomedical science, demonstrating a strong commitment to technological innovation and impactful healthcare research.

Profile: Scopus

Featured Publication

Raus-Jarząbek, E. (2025). A practical guide to ECG device performance testing according to international standards. Electronics (Open access).

 

Assist. Prof. Dr. Manea Almatared | Civil Engineering | Civil Engineering Award

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Assist. Prof. Dr.Manea Almatared | Civil Engineering | Civil Engineering Award

Assist. Prof. Dr. Manea Almatared | Najran University | Saudi Arabia

Assist. Prof. Dr. Manea Mohammed Saleh Almatared is an Assistant Professor in the Civil Engineering Department at the Engineering School, Najran University, specializing in the application of advanced technologies in infrastructure, construction, and facility management. He holds a Ph.D. and M.Sc. in Civil Engineering from Western Michigan University, USA, and a B.Sc. in Civil Engineering from Najran University, KSA. His research interests encompass Digital Twin technologies in construction and infrastructure, Building Information Modeling (BIM), real-time virtual and physical data integration, and predictive maintenance driven by the Internet of Things (IoT) and Artificial Intelligence (AI). Dr. Almatared has produced impactful scientific contributions with 121 citations from 117 citing documents, 4 published documents, and an h-index of 4, reflecting the academic strength and relevance of his work. His research aims to improve efficiency, sustainability, and safety in built environments through the adoption of cutting-edge digital solutions in civil engineering. Alongside his research, he has contributed to academic and administrative roles, including involvement in scientific research committees, accreditation and quality teams, student development, and community engagement. With both academic and practical experience—enhanced by his work as Assistant Project Manager and Research Assistant at Western Michigan University—Dr. Almatared remains dedicated to academic excellence, technological innovation, and preparing the next generation of civil engineers to meet future industry challenges.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Hakimi, O., Liu, H., Abudayyeh, O., Houshyar, A., Almatared, M., & Alhawiti, A. (2023). Data fusion for smart civil infrastructure management: A conceptual digital twin framework. Buildings, 13(11), 2725.

Almatared, M., Liu, H., Abudayyeh, O., Hakim, O., & Sulaiman, M. (2023). Digital-twin-based fire safety management framework for smart buildings. Buildings, 14(1), 4.

Almatared, M., Liu, H., Tang, S., Sulaiman, M., Lei, Z., & Li, H. X. (2022). Digital twin in the architecture, engineering, and construction industry: A bibliometric review. Construction Research Congress 2022, 670–678.

Tang, S., Liu, H., Almatared, M., Abudayyeh, O., Lei, Z., & Fong, A. (2022). Towards automated construction quantity take-off: An integrated approach to information extraction from work descriptions. Buildings, 12(3), 354.

Almatared, M. (2024). An integrated digital twin framework and evacuation simulation system for enhanced safety in smart buildings. Doctoral dissertation, Western Michigan University.

Dr. Partha Ghosh | AI in Engineering | Best Researcher Award

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Dr. Partha Ghosh | AI in Engineering | Best Researcher Award

Dr. Partha Ghosh | Netaji Subhash Engineering College | India

Dr. Partha Ghosh is a seasoned academic and researcher with more than 22 years of professional experience in Computer Science and Information Technology, currently serving as Associate Professor in the Department of Information Technology and Head of the Department of Computer Science and Business Systems at Netaji Subhash Engineering College, Kolkata. His research expertise spans Computer Networking, Machine Learning, Cloud Computing, Intrusion Detection Systems, Optimization Algorithms, Feature Selection and Classification Techniques, with a focus on developing secure, intelligent and high-performance cloud-based computational environments. His scholarly impact is reflected through 16 SCOPUS-indexed documents, 194 citations by 173 documents and an h-index of 7. Additionally, his ORCID profile lists 20 research works, and according to Google Scholar he has 333 citations (244 since 2020), an h-index of 10 (9 since 2020) and an i10-index of 10 (9 since 2020), demonstrating consistent and growing research visibility. To date, he has authored 24 publications including indexed journal papers, international conference papers and book chapters. He has taught a wide range of core and advanced courses such as Computer Organisation, Computer Networks, Advanced Computer Networking, Microprocessors and Microcontrollers and Database Management Systems at undergraduate and postgraduate levels. His academic engagement also includes serving as Editor-in-Chief and Editorial Board Member of reputed journals and holding multiple Fellow and Life Membership roles across professional bodies, underscoring his continued commitment to research innovation, knowledge dissemination and academic leadership.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Ghosh, P., Mandal, A. K., & Kumar, R. (2015). An efficient cloud network intrusion detection system. In Information Systems Design and Intelligent Applications: Proceedings of …

Ghosh, P., Karmakar, A., Sharma, J., & Phadikar, S. (2018). CS-PSO based intrusion detection system in cloud environment. In Emerging Technologies in Data Mining and Information Security: Proceedings …

Ghosh, P., & Mitra, R. (2015). Proposed GA-BFSS and logistic regression based intrusion detection system. In Proceedings of the 2015 Third International Conference on Computer …

Ghosh, P., Sarkar, D., Sharma, J., & Phadikar, S. (2021). An intrusion detection system using modified-firefly algorithm in cloud environment. International Journal of Digital Crime and Forensics, 13(2), 77–93.

Ghosh, P., Debnath, C., Metia, D., & Dutta, R. (2015). An efficient hybrid multilevel intrusion detection system in cloud environment. IOSR Journal of Computer Engineering, 16(4), 16–26.

Ghosh, P., Shakti, S., & Phadikar, S. (2016). A cloud intrusion detection system using novel PRFCM clustering and KNN based dempster-shafer rule. International Journal of Cloud Applications and Computing, 6(4), 18–35.