About me
Iyinoluwa Oyelade, PhD is a Nigerian academic, researcher, and lecturer in Information Technology at the Federal University of Technology, Akure (FUTA). She holds a PhD in Computer Science with specialization in Artificial Intelligence ad its applications, Internet of Things (IoT) and Computer Vision. Her research focuses on applying AI and IoT to real-world problems, particularly in smart agriculture, Education, and healthcare systems.
She has published in reputable international journals and IEEE conferences, including Scopus-indexed outlets, and has presented her work at major global platforms such as PyCon US, ICTP Trieste, and IEEE conferences. Beyond research, she teaches core computing courses at both undergraduate and postgraduate levels and actively contributes to university administration and professional bodies such as the Computer Professionals of Nigeria, ISACA, and OWSD. Her work reflects a strong commitment to sustainable technology, capacity building, and impactful innovation in developing contexts.
Dr. Iyinoluwa Moyosola Oyelade is a Nigerian computer scientist, researcher, and academic specializing in Artificial Intelligence, Internet of Things (IoT), Deep Learning, and Intelligent Software Systems. She currently serves as a Lecturer II in the Department of Information Technology at the Federal University of Technology, Akure (FUTA), Nigeria, where she contributes to teaching, research, and innovation in emerging computing technologies. With a strong interdisciplinary outlook, her work focuses on applying intelligent systems to address real-world challenges in agriculture, healthcare, and smart environments.
Dr. Oyelade obtained her Ph.D. in Computer Science from the Federal University of Technology, Akure in 2024, where her doctoral research developed an Internet of Things–based farmland intrusion detection model that integrates computer vision and intelligent monitoring technologies to enhance agricultural security. She previously earned an M.Tech. in Computer Science (2018) from the same institution, where she developed a stock market trend prediction model using data mining techniques, and a B.Sc. in Computer Science (Information Systems) from Babcock University, Nigeria in 2014. Her academic training has been complemented with professional certifications in Data Analysis (Cisco Networking Academy), Information Systems Auditing and Assurance (Coursera), Big Data Analytics with Excel and SQL, and Computer Security Administration.
Dr. Oyelade’s professional career at FUTA spans both academic and technical roles, reflecting her strong foundation in both theory and practice. Prior to joining the academic faculty, she served as a System Analyst (2015–2022) and later Senior System Analyst (2022–2023) at the Computer Resource Centre of FUTA, where she contributed to institutional digital infrastructure, system integration, and technology management. She joined the Department of Information Technology as an Assistant Lecturer in 2023 and was promoted to Lecturer II in 2024.
As an educator, Dr. Oyelade teaches a wide range of undergraduate and postgraduate courses including Social and Professional Issues in Information Technology, Internet of Things and its Applications, Information Technology Infrastructure, Data Structures and Algorithms, Operating Systems, Programming Languages, and Object-Oriented Application Development. She is recognized for her student-centered teaching approach, mentorship, and commitment to academic excellence. Her dedication to teaching and student engagement earned her the Female Lecturer of the Year Award in the Department of Information Technology at FUTA for the 2024/2025 and 2025/2026 academic sessions.
Dr. Oyelade’s research lies at the intersection of Artificial Intelligence, IoT and Computer Vision, with a focus on building intelligent systems that solve societal challenges. Her work has explored areas such as smart agriculture, animal intrusion detection, livestock disease diagnosis, healthcare monitoring systems, and AI-powered analytics. She has authored and co-authored numerous peer-reviewed journal and conference publications in reputable outlets, including Scopus-indexed journals and IEEE conferences. Her recent research contributions include work on deep learning-based animal intrusion detection, domain-aware AI frameworks for livestock disease detection and care recommendation, Li-Fi–based patient monitoring systems, and facial emotion recognition for children with autism spectrum disorder.
Her research impact has been recognized through several competitive awards and grants. In 2026, she received the Professor Alex Acholonu FAS Prize for Poster Presentation Award at the 7th NAS Annual Scientific Conference for her work on lightweight deep learning models for banana leaf disease detection aimed at improving food security. She also received the TETFund Institution-Based Research Grant (2025) as the principal investigator to lead a research titled “LivestockGuardian: A Mobile-Based Livestock Disease Detection and Care Recommendation System.” Additionally, she was awarded a Speaker Grant to present at PyCon US 2025 in Pittsburgh, USA, highlighting her contributions to the global Python and open-source community.
Dr. Oyelade actively participates in the international research community through conferences, workshops, and collaborative initiatives. She has presented or participated in several global academic events including the Python Conference (PyCon) US 2025, the IEEE International Conference on Electro-Computing Technologies, and the International Conference of the Society for the Advancement of ICT & Comparative Knowledge (SOCTHADICK). She also attended the AfricaConnect IoT Research Workshop and the “Investing in Africa’s Climate Future” workshop at the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy, where she engaged with global researchers on data infrastructure and IoT-based research data acquisition.
Beyond teaching and research, Dr. Oyelade plays active roles in academic administration and professional service. She serves as Welfare Officer in the Department of Information Technology, Technical Officer of the Organization for Women in Science for the Developing World (OWSD) FUTA Branch, and a member of several committees within the School of Computing, including the Strategic Planning Committee, Sports Committee, and Annual Lecture Committee. She also supervises students in entrepreneurship courses and serves as a course adviser and registration officer within the department.
Her professional affiliations include membership in the Computer Professionals Registration Council of Nigeria (CPN), the Information Systems Audit and Control Association (ISACA), and the Organization for Women in Science for the Developing World (OWSD). Through these platforms, she contributes to the advancement of computing research, digital innovation, and the empowerment of women in science and technology.
Outside academia, Dr. Oyelade demonstrates strong leadership and community engagement. She serves as Chairperson of the Planning Committee for the Woman-to-Woman Conference at House on the Rock Church in Akure, where she contributes to initiatives focused on leadership development and community empowerment.
Through her combined work in teaching, research, innovation, and service, Dr. Oyelade continues to advance the application of artificial intelligence and IoT technologies to address pressing societal challenges, particularly in agriculture, healthcare, and smart systems development. Her career reflects a commitment to academic excellence, impactful research, and the development of technology-driven solutions for sustainable development in Africa and beyond.
Other Memberships/Affiliations
Degrees:
viii. Iyinoluwa Oyelade, Emmanuel, A., Tunde Joshua Fatoke, & Boyinbode Olutayo K. (2025). Deep Learning-Based Facial Emotion Recognition for Children with Autism Spectrum Disorder. Proceedings of the 5th International Conference of the Society for the Advancement of ICT & Comparative Knowledge (SOCTHADICKconf’25), 48–62. https://www.researchgate.net/publication/399886533_Deep_Learning-Based_Facial_Emotion_Recognition_for_Children_with_Autism_Spectrum_Disorder
Oyelade, I. M., Ugwu, C. C., Oladoja, I. P., Ugwu, T. A., Akinbo, R. S., Makinde, I. A., & Faluyi, O. B. (2025). An integrated Mask R-CNN and domain aware RAG-enabled LLM framework for livestock disease detection and care recommendation. International Journal of Computer Applications, 187(33), 25–36. https://doi.org/10.5120/ijca2025925602
I. M. Oyelade, O. Ayomide Madamidola, O. K. Boyinbode and J. Olamatanmi Mebawondu, (2024) "An Improved Feature Extraction Approach for Convolutional Neural Networks Based Animal Intrusion Detection Models," 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON), Ado Ekiti, Nigeria, pp. 1-5, DOI: 10.1109/NIGERCON62786.2024.10926970.
iii. Iyinoluwa M. Oyelade, Olutayo K. Boyinbode, Olumide S. Adewale, Emmanuel O. Ibam, (2024) "Farmland Intrusion Detection using Internet of Things and Computer Vision Techniques", International Journal of Information Technology and Computer Science (IJITCS), 16(2), pp. 32-44. DOI:10.5815/ijitcs. 2024.02.03
Oyelade Iyinoluwa and Adewale Olumide S. (2019) “Stock Market Trend Prediction Model Using Data Mining Techniques.” Current Trends in Computer Science and Applications; 1(5), 119 -127
Oyelade I. M., Boyinbode O. K. and Adewale O.S. (2023) “A Review of Existing Farmland Intrusion Detection Systems” International Journal of Computer Applications; 185(22), 41-46