Amena Darwish | Machine learning | Best Researcher Award

Published on by Academic Achievements

Ms. Amena Darwish | Machine learning | Best Researcher Award

Ms. Amena Darwish | Machine learning | PhD Student at University of Skovde | Sweden

Ms. Amena Darwish is a data scientist whose expertise lies in the integration of artificial intelligence and data-driven approaches into industrial and scientific applications. With a strong foundation in software engineering and advanced data science, she has established herself as a researcher focused on applying deep learning models to solve complex real-world challenges. Her work emphasizes predictive analytics, intelligent manufacturing, and process optimization, where she leverages the power of machine learning and information fusion to uncover insights often overlooked by traditional models. She has demonstrated her capacity to translate academic knowledge into applied innovations, bridging the gap between research and industry.

Academic Profile

Scopus |  ORCID

Education

Ms. Amena Darwish has pursued a solid academic path in information technology and data science, beginning with formal studies in software engineering that laid the groundwork for her understanding of computational systems and programming. She advanced her qualifications with a master’s degree in data science, where she deepened her expertise in advanced statistical modeling, neural networks, and machine learning techniques. Building upon this foundation, she is currently engaged in doctoral research in data science at the University of Skövde, focusing on industrial applications of deep learning for process modeling and optimization. Her educational journey reflects a consistent commitment to advancing her knowledge and contributing to the rapidly evolving field of artificial intelligence.

Experience

Ms. Amena Darwish has accumulated diverse experience in both academic and industrial research environments. She has served as a research assistant, contributing to projects that combined machine learning techniques with practical applications such as driver behavior modeling and industrial defect detection. Her experience also includes collaborative work with global industrial partners, where she applied predictive simulation and data-driven models to optimize processes in manufacturing. Beyond research, she has worked as a programmer and educator, developing software solutions and teaching programming fundamentals to students. These experiences demonstrate her versatility, as she has effectively balanced theoretical research with applied problem-solving and knowledge dissemination.

Research Interest

Ms. Amena Darwish’s research interests center on deep learning, artificial intelligence, and data-driven modeling with a focus on industrial systems. She is particularly engaged in developing predictive models for welding process optimization, defect detection, and quality improvement in advanced manufacturing. Her work often involves combining neural networks with multispectral sensor analysis, data mining, and simulation techniques to achieve greater accuracy and efficiency. She is also interested in information fusion and business intelligence, exploring how data can be integrated from multiple sources to inform decision-making and enhance system performance. Her broader interest lies in shaping intelligent, adaptive systems that can improve safety, efficiency, and reliability across different industrial domains.

Award

Ms. Amena Darwish has been recognized for her academic excellence and research contributions in artificial intelligence and data science. Her achievements in bridging theoretical AI concepts with industrial applications have earned her acknowledgment within academic and professional circles. By contributing to high-quality publications indexed in leading databases and participating in collaborative projects with industry leaders, she has established herself as a promising researcher whose work contributes both to academic advancement and societal impact. Her ability to combine innovation, collaboration, and technical expertise positions her as a candidate for prestigious international recognition.

Selected Publication

  • Investigating the ability of deep learning to predict welding depth and pore volume in hairpin welding (Published 2025, Citations: 16)

  • Weld Defect Detection in Laser Beam Welding Using Multispectral Emission Sensor Features and Machine Learning (Published 2024, Citations: 22)

  • Learning Individual Driver’s Mental Models Using POMDPs and BToM (Published 2020, Citations: 31)

Conclusion

Ms. Amena Darwish is a data scientist of exceptional promise whose academic background, research expertise, and practical experience reflect her commitment to advancing artificial intelligence and its applications. Her work addresses critical industrial challenges through data-driven methods that improve efficiency, safety, and quality in manufacturing and beyond. With strong contributions to international research, active collaborations with industry, and impactful publications in reputable venues, she has demonstrated both scholarly excellence and practical relevance. Ms. Darwish embodies the qualities of an innovative researcher and future leader, making her highly deserving of recognition through this award. Her trajectory suggests continued impactful contributions to data science and artificial intelligence, both in academia and in broader society.

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.