Alaba Ayotunde Fadele | Computer Science | Research Excellence Award

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Dr. Alaba Ayotunde Fadele | Computer Science | Research Excellence Award

Dr. Alaba Ayotunde Fadele | Federal University of Education | Nigeria

Dr. Alaba Ayotunde Fadele is a distinguished computer scientist and academic leader whose work spans blockchain, cybersecurity, IoT systems, and smart contract security. He is currently a Post-Doctoral Fellow at the Instituto de Estudos e Desenvolvemento de Galicia (IDEGA), Madrid, Spain, beginning in 2025. He holds two Ph.D. degrees: a Ph.D. in Computer Science with a specialization in Blockchain from the International University, Bamenda (2020–2023), where his research focused on smart contracts and cyber security, and a Ph.D. in Computer Science from the University of Malaya (2016–2019), specializing in IoT and cyber security. His earlier academic foundations include a Master of Computer Science (2011–2014) from Ahmadu Bello University, a Postgraduate Diploma in Education (2011–2012) from Usman Danfodio University, and a First Class Honours Bachelor’s degree in Computer Science (2004–2008) from Nasarawa State University. Dr. Fadele has held major administrative and academic leadership roles, including Director of the ICT Unit at the Federal University of Education, Zaria (from October 2025), Head of the Department of Computer Science (from June 2025), and Head of the Communications Advancement Unit in the Directorate of University Advancement (2024–2025). He has served as a full-time lecturer at the Federal University of Education, Zaria since 2010, a visiting lecturer at St. Francis of Assisi College of Education since 2021, and previously as a lecturer at the Federal Polytechnic Bauchi, as well as a Research Assistant at the University of Malaya. His outstanding contributions have earned him the 2019 JNCA Best Survey Paper Award, Best Presenter Award at the Faculty of Computer Science and Information Technology Postgraduate Symposium in Malaysia (2017), and recognition as the Best Graduating Student in Computer Science at Nasarawa State University (2007/2008). Dr. Fadele has authored 20 scholarly publications, accumulating 1,253 citations from 1,245 documents, and holds an h-index of 10, reflecting his impactful contributions to cyber security, IoT research, blockchain systems, and advanced computing innovations.

Profiles: Scopus | Orcid 

Featured Publications

Alaba, F. A., & Rocha, A. (2025). Conclusions, future directions, and recommendations. In F. A. Alaba & A. Rocha (Eds.), Studies in Systems, Decision and Control (Chapter 5). Springer.

Alaba, F. A., & Rocha, A. (2025). Implementation results. In F. A. Alaba & A. Rocha (Eds.), Studies in Systems, Decision and Control (Chapter 4). Springer.

Alaba, F. A., & Rocha, A. (2025). Machine learning algorithms on malware detection against smart wearable devices. In F. A. Alaba & A. Rocha (Eds.), Studies in Systems, Decision and Control (Chapter 3). Springer.

Alaba, F. A., & Rocha, A. (2025). Security challenges of wearable technology. In F. A. Alaba & A. Rocha (Eds.), Studies in Systems, Decision and Control (Chapter 2). Springer.

Xiao Li | Signal Processing and Pattern Recognition | Best Researcher Award

Published on by Invention Awards

Dr. Xiao Li | Signal Processing and Pattern Recognition | Best Researcher Award

Dr. Xiao Li | School of Computer Science, Xidian University | China

Dr. Xiao Li is an accomplished researcher and Associate Professor at the School of Computer Science and Technology, Xidian University, Xi’an, China, where he also serves as a Master’s Supervisor. He obtained his Ph.D. in Computer Science from Xidian University in 2017. His research focuses on AI for Science, biomedical intelligent diagnosis, radar signal recognition, and cryptographic intelligence analysis, with a strong emphasis on advancing artificial intelligence for complex real-world applications. Dr. Li has made significant contributions to machine learning and signal processing, particularly through the development of pseudo-supervised contrastive learning frameworks and unknown sample generation algorithms that enhance classification accuracy for both known and unknown classes in open-set visual recognition. He has also designed innovative visual prototype generation networks and asymmetric variational autoencoder (VAE) models to improve cross-modal distribution alignment, yielding notable progress in RGB-D transfer learning and fine-grained image recognition. His interdisciplinary research extends to biomedical and engineering domains, including ECG-based cardiomyopathy detection, EEG-based emotion recognition, radar emitter identification, and cryptanalysis. Dr. Li has led multiple research projects funded by the National Natural Science Foundation of China (NSFC), the China Postdoctoral Science Foundation, and the Shaanxi Provincial Natural Science Fund, and has participated in several national and provincial collaborative projects. With an impressive academic record of over 60 peer-reviewed publications, his work has garnered more than 1,200 citations and an h-index of 18, reflecting his growing influence in artificial intelligence and computational intelligence research.

Featured Publications

Zhao, Z., Li, X., & Chang, Z., & Hu, N. (2025). Multi-view contrastive learning with maximal mutual information for continual generalized category discovery. Expert Systems with Applications, 259, 125994.

Zhao, Z., Li, X., Zhai, Z., & Chang, Z. (2024). Pseudo-supervised contrastive learning with inter-class separability for generalized category discovery. Knowledge-Based Systems, 287, 111477.

Zhao, Z., Jiang, M., Guo, J., Yang, X., Hu, Y., & Zhou, X. (2022, October 9). Raindrop removal for in-vehicle camera images with generative adversarial network. 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 9945304. IEEE.