Angelique Nicolas Messi | Computational Chemistry | Best Researcher Award

Published on by ScienceFather

Dr. Angelique Nicolas Messi | Computational Chemistry | Best Researcher Award

Department of Organic Chemistry, University of Yaoundé 1 | Cameroon

Dr. Angelique Nicolas Messi is a distinguished Cameroonian organic chemist and Senior Lecturer at the Department of Organic Chemistry, University of Yaoundé I. He also serves as the Head of the Department of Basic and Applied Fundamental Sciences at the State University of Ebolowa, where he teaches general and organic chemistry, analytical chemistry, thermodynamics, pharmacognosy, and research methodology. Dr. Messi earned his Ph.D. in Organic Chemistry from the University of Yaoundé I in 2018, focusing on the isolation, structural elucidation, and bioassay of biflavonoids from Ochna species. He expanded his expertise as a Postdoctoral Research Fellow at the University of Fribourg in Switzerland under Prof. Christian G. Bochet (2020–2021) and through research visits to the University of Free State, South Africa (2015, 2023). His research centers on pharmaceutical and natural product chemistry, emphasizing drug discovery from natural sources, including biflavonoids, flavonoids, and synthetic analogs with neuroprotective, anticancer, antimalarial, and anti-HIV properties. A prolific researcher and mentor, Dr. Messi has authored numerous peer-reviewed articles in high-impact journals such as Pharmaceutics, Molecular Neurobiology, Chemistry Africa, and Biochemical Systematics and Ecology. His scientific impact is reflected in 122 citations, an h-index of 5, and an i10-index of 4. Renowned for his expertise in chromatographic and spectroscopic techniques such as NMR, LC-MS, and HRESIMS, he has contributed significantly to the discovery of novel bioactive compounds. In recognition of his outstanding contributions, he was honored as the “Best Researcher in Organic Chemistry” in 2025 by the Research Chemistry Awards. Fluent in both French and English, Dr. Messi is an invited speaker at international conferences, a reviewer for leading scientific journals, and a strong advocate for green chemistry and sustainable drug development in Africa.

Profile: Google Scholar | Scopus

Featured Publications

  • Ndongo, J. T., Issa, M. E., Messi, A. N., Ngo Mbing, J., Cuendet, M., & Pegnyemb, D. E. (2015). Cytotoxic flavonoids and other constituents from the stem bark of Ochna schweinfurthiana. Natural Product Research, 29(17), 1684–1687.

  • Messi, A. N., Ngo Mbing, J., Ndongo, J. T., Nyegue, M. A., Tchinda, A. T., Yemeda, F. L., & Pegnyemb, D. E. (2016). Phenolic compounds from the roots of Ochna schweinfurthiana and their antioxidant and antiplasmodial activities. Phytochemistry Letters, 17, 119–125.

  • Djova, S. V., Nyegue, M. A., Messi, A. N., Afagnigni, A. D., & Etoa, F. X. (2019). Phytochemical study of aqueous extract of Ochna schweinfurthiana F. Hoffm powder bark and evaluation of their anti-inflammatory, cytotoxic, and genotoxic activities. Evidence-Based Complementary and Alternative Medicine, 2019, 8908343.

  • Messi, A. N., Bonnet, S. L., Owona, B. A., Wilhelm, A., Kamto, E. L. D., Ndongo, J. T., & Pegnyemb, D. E. (2022). In vitro and in silico potential inhibitory effects of new biflavonoids from Ochna rhizomatosa on HIV-1 integrase and Plasmodium falciparum. Pharmaceutics, 14(8), 1701.

  • Owona, B. A., Mary, A., Messi, A. N., Ravichandran, K. A., Mbing, J. N., & Pegnyemb, E. (2025). Biflavonoid methylchamaejasmin and Khaya grandifoliola extract inhibit NLRP3 inflammasome in THP-1 cell model of neuroinflammation. Molecular Neurobiology, 62(2), 1605–1619.

Ms. Shakila Rahman | Computer Engineering | Best Researcher Award

Published on by ScienceFather

Ms. Shakila Rahman | Computer Engineering | Best Researcher Award

Ms. Shakila Rahman , American International University – Bangladesh (AIUB)

Shakila Rahman 🇧🇩 is a Lecturer at the Department of Computer Science, AIUB 🏫. She earned her M.Sc. in AI & Computer Engineering 🤖 from the University of Ulsan, South Korea 🇰🇷, with a CGPA of 4.00/4.50 📚. Her research spans UAV networking 🚁, wireless sensor networks 🌐, and machine learning 🧠. She actively supervises undergraduate projects 👩‍💻 and has published in reputed journals 📖. With experience in academia and applied computing, she contributes passionately to AI and emerging technologies 💡. She is also active on ResearchGate, ORCID, and LinkedIn 🌍.

Professional Profile:

Orcid

🔹 Education & Experience 

Shakila Rahman completed her M.Sc. in AI & Computer Engineering 🎓 from the University of Ulsan, South Korea 🇰🇷, and her B.Sc. in Computer Science & Engineering from IIUC 🇧🇩. Currently, she serves as a Lecturer 👩‍🏫 at American International University-Bangladesh (AIUB), where she teaches core computing subjects 💻. She previously worked as a Graduate Research Assistant in South Korea 🔬 and as an Undergraduate Teaching Assistant at IIUC 👩‍🔬. Her experience blends international research, teaching, and real-world project supervision 🌐. She is skilled in Python, Java, and C++ 🧑‍💻, and fosters student innovation through hands-on learning 🧪.

🔹 Professional Development

Shakila has enhanced her technical skillset through multiple workshops and training programs 🛠️. She completed web development 💻 at BASIS BITM, mobile game/app development 🎮 with Bangladesh ICT Division, and Android app development 🤖 with Affable Technology. As a competitive programmer, she participated in ICPC, NGPC, and UVA online judges 🏆. During her B.Sc., she joined industrial events like the Tech Summit by LEADS Corporation 👩‍💼. These initiatives equipped her with a dynamic understanding of both academic theory and industry practices 🌐. She is also an IELTS-certified English communicator with a score of 6.0 🇬🇧🗣️.

🔹 Research Focus

Shakila Rahman’s research lies at the intersection of AI and networking technologies 🤖🌐. Her main focus includes UAV Networking 🚁, Wireless Sensor Networks 🛰️, Network Systems 🖧, and Optimization Algorithms 🔁. She also works with Machine Learning 🧠, Deep Learning 🧬, Image Processing 🖼️, and AR/VR applications in AI 🥽. Her work emphasizes solving real-world challenges like obstacle detection, fire/smoke recognition 🔥🚨, and human activity classification 🚶‍♂️🚨. She publishes in SCIE and SCOPUS-indexed journals 📚, contributing to the academic community with innovations in intelligent systems and computer vision 👁️‍🗨️.

🔹 Awards & Honors 

Shakila Rahman has received prestigious academic honors 🏅, including the Brain Korea Scholarship (BK21) 💰 during her M.Sc. at the University of Ulsan 🇰🇷. She was also awarded the fully funded AF1 Scholarship worth approx. $21,000 USD 🎓💵. Her research was supported by the National Research Foundation of Korea (NRF) 🇰🇷 for multiple publications 📑. These recognitions highlight her academic excellence 🌟, research potential 🔬, and commitment to global scientific collaboration 🌍. Her accolades stand as a testament to her dedication and passion in the field of Artificial Intelligence and networked systems 🚀📡.

Publication Top Notes:

🧠 Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets

  • Journal: Journal of Imaging

  • Year: 2025

  • Citations: 0

  • Summary: This study presents a YOLOv11-based model designed for real-time recognition of Bangla and English sign language alphabets. The model aims to bridge communication gaps for the hearing-impaired by accurately detecting and classifying sign language gestures.

🏙️ Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11

  • Journal: Future Internet

  • Year: 2025

  • Citations: 0

  • Summary: This research introduces an AI-driven approach utilizing YOLOv11 for detecting infrastructure faults in urban environments. The model enhances city safety by enabling prompt identification and maintenance of structural issues.

🧴 A Hybrid CNN Framework DLI-Net for Acne Detection with XAI

  • Journal: Journal of Imaging

  • Year: 2025

  • Citations: 0

  • Summary: The study proposes DLI-Net, a hybrid CNN framework combining DeepLabV3 for segmentation and InceptionV3 for classification, to detect and classify acne lesions. Achieving a 97% accuracy rate, the model incorporates Grad-CAM for explainable AI, enhancing transparency in dermatological diagnostics.

🚁 A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment

  • Journal: Electronics

  • Year: 2025

  • Citations: 0

  • Summary: This paper presents a Deep Q-Learning algorithm for unmanned aerial vehicles (UAVs) to navigate efficiently within constrained wireless sensor networks. The approach optimizes detouring strategies, enhancing UAV performance in complex environments.

🛡️ A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification

  • Journal: Iranian Journal of Electrical and Electronic Engineering

  • Year: 2024

  • Citations: 0

  • Summary: The research develops a YOLOv9-based deep learning model to classify human activities as violent or non-violent. Aimed at enhancing surveillance systems, the model contributes to improved public safety through accurate activity detection.

✅ Conclusion

Shakila Rahman is a highly suitable candidate for the Best Researcher Award due to her blend of academic excellence, innovative research, international exposure, and impactful contributions to AI and networking fields. Her work not only advances fundamental knowledge but also targets practical challenges, demonstrating a research trajectory with strong future promise. Recognizing her with this award would celebrate her achievements and encourage continued excellence in emerging technology domains. 🚀🤖📡