Jacob Mbarndouka Taamté | Machine learning | Best Researcher Award

Published on by Academic Achievements

Dr. Jacob Mbarndouka Taamté | Machine learning | Best Researcher Award

Dr. Jacob Mbarndouka Taamté | Machine learning – Research Officer at Institute of Geological and Mining Research, Cameroon

Jacob Mbarndouka Taamté is an accomplished research scientist specializing in electronics, electrical engineering, automation, instrumentation, and industrial maintenance. Based in Cameroon, his work has been instrumental in the development of low-cost, innovative devices for monitoring air quality, environmental radiation, and nuclear safety. Taamté holds a Ph.D. in Physics, with a focus on Electrical and Electronic Systems, and has made significant contributions to the field of environmental monitoring through cutting-edge technology. His academic and professional journey is marked by numerous achievements, including being awarded the Best Young Researcher of Cameroon in 2024. He is also an active member of several international research initiatives and has presented his findings at numerous conferences, advancing global discussions on sustainable technology and environmental protection.

Profile:

Orcid

Education:


Jacob Mbarndouka Taamté’s academic journey is defined by rigorous studies in the fields of physics, electrical engineering, and industrial production. He completed his Ph.D. in Physics, specializing in Electrical and Electronic Systems, at the University of Yaoundé I in 2022. Prior to this, he earned his Master’s in Science from the University of Ngaoundéré, where he also completed his Bachelor’s and UDT degrees, specializing in industrial maintenance and production. His educational background, spanning over a decade, has provided him with a solid foundation in the development and application of advanced technologies aimed at solving complex industrial and environmental challenges.

Experience:


Jacob Taamté’s professional career spans several years in both academia and research. Since 2021, he has served as a Research Officer at the Research Center for Nuclear Science and Technology (CRSTN) at the Institute of Geological and Mining Research (IRGM) in Cameroon, where he continues to contribute to innovative research on environmental monitoring and radiation protection. He also teaches Electronics and Electrical Engineering at The Armandins Higher Institute in Yaoundé, Cameroon, guiding students in practical applications of his research. Before his current roles, Taamté worked as a teacher and supervisor in scientific clubs, mentoring young minds and promoting scientific inquiry. His work extends beyond research, as he actively engages in the development of programs aimed at promoting sustainable technological solutions in his region.

Research Interest:


Jacob Taamté’s primary research interests lie in the areas of environmental monitoring, nuclear instrumentation, and sustainable technology. He is particularly focused on the development of low-cost electronic devices for real-time monitoring of air quality, water quality, soil health, and environmental radiation. His work integrates the use of microcontrollers, embedded systems, and machine learning to design smart devices that provide real-time data for public health and safety. Taamté’s research in this domain has led to practical applications, such as radiation protection systems and air quality monitoring devices, which have been widely recognized for their impact on public health and safety, especially in Cameroon and other African countries.

Award:


Jacob Mbarndouka Taamté has earned numerous accolades for his groundbreaking research and contributions to the scientific community. In 2024, he was awarded the Special Prize at the National Technology Days in Cameroon for his innovative research in environmental monitoring. He also received the Best Young Researcher of Cameroon Award the same year, recognizing his outstanding contributions to research and technology. Additionally, Taamté was honored with the Best Young Professional Radiation Protection Scientist Award in 2022 by the International Radiation Protection Association (IRPA), reflecting his exceptional work in the field of environmental radiation measurement. His achievements underscore his leadership in scientific research and his dedication to improving public health through technology.

Publications:


Jacob Taamté has authored several influential publications in renowned scientific journals, contributing significantly to the fields of environmental monitoring, radiation protection, and low-cost technological innovations. Below are some of his key publications:

  1. Taamté, J. M., Danwé, Y. F., Folifack Signing, V. R., Gondji, D. S., Koyang, F., & Saïdou. (2025). Design of a low-cost water quality assessment device based on a reference instrument. Urban Water Journal, 1–22. [Cited by: 15]
  2. Taamte, J. M., Tchuente Siaka, Y. F., Nducol, N., Yakum-Ntaw Younui, S., Ahmadou, G., Etende Essama, R. C., … Saïdou. (2025). Smart electronic device for air quality and exposure risk assessment. Smart Science, 1–15. [Cited by: 12]
  3. Folifack Signing, V. R., Taamté, J. M., & Saïdou. (2024). IoT-based Monitoring System and Air Quality Prediction Using Machine Learning for a Healthy Environment in Cameroon. Environmental Monitoring and Assessment, 198(12). [Cited by: 25]
  4. Taamté, J. M., Kountchou Noube, M., Folifack Signing, V. R., Yerima Abba Hamadou, et al. (2024). Real-time air quality monitoring based on locally developed unmanned aerial vehicle and low-cost smart electronic device. Journal of Instrumentation, 19 P05036. [Cited by: 18]
  5. Taamté, J. M., Koyang, F., Gondji, D. S., Oumar Bobbo, M., et al. (2022). Low-cost radon monitoring with validation by a reference instrument. Instrumentation Science and Technology. [Cited by: 22]
  6. Taamté, J. M., Kountchou Noubé, M., Bodo Bertrand, et al. (2021). Low-cost air quality monitoring system design and comparative analysis with a conventional method. International Journal of Energy and Environmental Engineering, 10(4). [Cited by: 10]

Conclusion:


Jacob Mbarndouka Taamté stands out as a researcher whose work combines scientific excellence, innovative problem-solving, and a commitment to societal impact. His research has not only contributed to the advancement of environmental monitoring technology but has also provided practical solutions to pressing global challenges such as radiation protection and public health. Through his numerous accolades, publications, and active participation in international projects, Taamté has established himself as a leader in his field. His dedication to advancing scientific knowledge, particularly in developing affordable technologies for environmental monitoring, makes him a deserving candidate for the Best Researcher Award.

Majdi Khalid | Machine learning | Best Researcher Award

Published on by Academic Achievements

Assoc Prof. Dr. Majdi Khalid | Machine learning | Best Researcher Award 

Associate Professor at Umm Al-Qura University

Assoc. Prof. Dr. Majdi Khalid is an esteemed researcher in the field of machine learning with a focus on deep learning, artificial intelligence, and their applications in various domains such as computer vision, natural language processing, and bioinformatics. He is currently an Associate Professor at Umm Al-Qura University, Makkah, Saudi Arabia. Dr. Khalid has made significant contributions to cutting-edge research, particularly in the intersection of AI and bioinformatics, publishing numerous papers in prestigious journals and collaborating with international researchers. His work in AI for drug discovery and healthcare highlights his dedication to using technology to solve complex biological and medical challenges.

Profile:

ORCID

Education:

Dr. Khalid holds a Ph.D. in Computer Science from Colorado State University, USA, which he completed in 2019. His doctoral research centered on advanced computational models and machine learning algorithms, laying the foundation for his future endeavors in AI and deep learning. Prior to his Ph.D., Dr. Khalid earned his Master of Computer Science (M.C.S.) from the same institution in 2013, and a Bachelor of Science (B.S.) in Computer Science from Umm Al-Qura University in 2006. His academic training has equipped him with the technical and theoretical expertise necessary to excel in both academia and applied research.

Experience:

Dr. Khalid’s academic career began as an Instructor at the Technical College in Al Baha, Saudi Arabia, from 2007 to 2008. After earning his graduate degrees, he joined Umm Al-Qura University as an Assistant Professor in 2019, where he has since been engaged in teaching and research. Throughout his academic journey, Dr. Khalid has focused on mentoring students, leading cutting-edge research projects, and publishing extensively in the areas of machine learning and AI. His collaboration with national and international research teams has further enriched his experience, making him a valuable contributor to the global AI research community.

Research Interests:

Dr. Khalid’s research interests span various applications of machine learning and deep learning. He specializes in developing computational models for computer vision, natural language processing, bioinformatics, and brain-computer interfaces. His work in AI-driven drug discovery has led to the development of innovative tools for identifying epigenetic proteins and other biomarkers, which are critical for advancing modern medicine. Dr. Khalid is also actively exploring how AI can enhance healthcare systems and improve diagnostic accuracy, with a strong focus on interdisciplinary collaboration between AI and biological sciences.

Awards:

Dr. Khalid has received numerous recognitions for his research excellence, including university-level awards for outstanding research performance. His contributions to the fields of AI and machine learning have been acknowledged by both academic institutions and international conferences. While he has yet to secure a large-scale international research award, his continued dedication to advancing the field positions him as a prime candidate for future accolades.

Publications:

  1. Ali, Farman, Abdullah Almuhaimeed, Majdi Khalid, et al. (2024). “DEEPEP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.” Methods.
    • Cited by articles focusing on the intersection of AI and drug discovery methodologies.
      Read the article here
  2. Khalid, Majdi, Farman Ali, et al. (2024). “An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.” Journal of Biomolecular Structure and Dynamics.
    • Cited by researchers investigating protein structure prediction and AI’s role in molecular biology.
      Read the article here
  3. Alsini, Raed, Abdullah Almuhaimeed, et al. (2024). “Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with 2D-CNN.” Journal of Biomolecular Structure and Dynamics.
  4. Alohali, Manal Abdullah, et al. (2024). “Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems.” Transactions on Emerging Telecommunications Technologies.
    • Cited in studies focusing on emotion recognition through AI in intelligent systems.
      Read the article here
  5. Majdi Khalid (2023). “Advanced Detection of COVID-19 through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis.” International Journal of Advanced Computer Science and Applications.
  1. Ali, Muhammad Umair, Majdi Khalid, et al. (2023). “Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.” Bioengineering, 10(12): 1430.
    • Cited by papers focusing on AI applications in medical diagnostics and image analysis for dermatology.
      Read the article here
  2. Alghushairy, Omar, Farman Ali, Wajdi Alghamdi, Majdi Khalid, et al. (2023). “Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.” Journal of Biomolecular Structure and Dynamics, 2023: 1-12.
    • Cited by studies dealing with protein-drug interactions and machine learning applications in bioinformatics.
      Read the article here
  3. Obayya, Marwa, Fahd N. Al-Wesabi, Rana Alabdan, Majdi Khalid, et al. (2023). “Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems.” IEEE Transactions on Consumer Electronics, 2023.
    • Cited by research on AI-enhanced traffic systems and predictive modeling in smart cities.
      Read the article here
  4. Alruwais, Nuha, Eatedal Alabdulkreem, Majdi Khalid, et al. (2023). “Modified Rat Swarm Optimization with Deep Learning Model for Robust Recycling Object Detection and Classification.” Sustainable Energy Technologies and Assessments, 59: 103397.
    • Cited by works in sustainable technologies and AI for recycling and waste management.
      Read the article here
  5. Adnan, Adnan, Wang Hongya, Farman Ali, Majdi Khalid, et al. (2023). “A Bi-Layer Model for Identification of piwiRNA using Deep Neural Learning.” Journal of Biomolecular Structure and Dynamics, 2023: 1-9.
  • Cited by articles focused on non-coding RNA identification and AI-driven molecular biology research.
    Read the article here

Conclusion

Assoc. Prof. Dr. Majdi Khalid is a highly deserving candidate for the Best Researcher Award due to his extensive research contributions in machine learning and artificial intelligence. His innovative work in applying machine learning to critical fields such as drug discovery, COVID-19 detection, and biomolecular prediction makes him a thought leader in his domain. With minor improvements in real-world application and cross-disciplinary collaboration, Dr. Khalid’s potential to lead global innovations in machine learning is undeniable. His current achievements already solidify his place as one of the leading researchers in his field, making him an outstanding candidate for this prestigious award.