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NeuroAI Innovative Insights

A Vision for Inclusive, Interdisciplinary AI-based Collaborative Research

Dr. Eng. Abdullatif BABA

As artificial intelligence revolutionizes the way we live, learn, and lead, human-driven cooperative innovation has never been so critical. In this context, LeadwitAI.net has been created to establish a platform that allows researchers and innovators from all corners of the globe and across different disciplines to work together, exchange ideas, and co-create the future of smart systems. As an AI researcher and academic passionate about robotics, embedded systems, and neuroadaptive technologies, I have witnessed both the immense potential and the challenges of working in silos. Progress in AI and smart systems should not be confined to isolated labs or single-discipline approaches. That’s why this platform is dedicated to fostering collaborative research that is

Through research briefs, calls to collaborate, opinion pieces, and blog entries, LeadwitAI.net will be an arena of knowledge sharing and voice for visionary, responsible AI leadership. If you’re a seasoned professor, an emerging scholar, or a policymaker with an interest in smart technologies, come walk this journey collaboratively, and boldly.

Research Directions for 2025: AI-Powered Innovation Across Domains:

Track 1: AI in Robotics & Cybersecurity

In this track, we explore the development of smart robotic systems for underwater and aerial applications, with a strong focus on security, robustness, and autonomous capabilities. Recent publications in this field:

Track 2: AI in Medical and Neuroscience Applications

This research track aims to bridge artificial intelligence and neuroscience for improved diagnostics, therapy, and disease management. Collaboration with neuroscience experts is actively sought. Recent publications in this field:

Current Collaborative Projects:

A collaborative project between the Gulf University for Science and Technology, Kuwait College of Science and Technology, and the University of Sialkot, titled “Web-based, AI-driven predictive platform for disease management,” has been established. This project is supported by the Seed Grant from Gulf University for Science and Technology – Graduate Studies and Research, under the umbrella of the Center for Applied Mathematics & Bioinformatics (CAMB), 2024.

A New Proposal:

I am currently developing an advanced robotized stereo radar system equipped with an FPGA-integrated Spinning Neuron Model (SPNM; https://lnkd.in/dmCeejg2) for real-time micro-UAV detection and classification. The goal of this project is to build a mobile, neuromorphic radar platform capable of 3D environmental sensing and intelligent target recognition under challenging outdoor conditions. The system combines:
1. Stereo radar sensing for precise range–angle localization,
2. FPGA acceleration for low-latency neuromorphic processing, and
3. AI-driven SPNM architecture inspired by biological neural resonance.
I am seeking collaboration with a research institute, startup, or technology company specializing in one or more of the following areas:
Radar and RF system design, FPGA and embedded AI hardware development, and mobile robotics and sensor fusion platforms.
If your organization is interested in co-developing or prototyping this system, integrating sensors, or providing industrial design and testing capabilities, please do not hesitate to contact me directly via LinkedIn or email. Let’s bridge AI, radar sensing, and robotics to create the next generation of autonomous detection systems.

https://www.linkedin.com/feed/update/urn:li:activity:7394461967425482753

A Selection of Recent Publications:

References:

  1. Baba, A. (2025). Robotized Stereo Radar System with FPGA-Integrated Spinning Neuron Model for Real-Time Micro-UAV Detection, SSRN, Paper 5924167, 2025. DOI: 10.2139/ssrn.5924167.
  2. Baba, A. (2025), Toward Adaptive Neurodynamics: A Spinning Neuron Model with Self-Organization and Quantum Synergy for Brain-Computer Interfaces, SSRN, (SSRN Scholarly Paper No. 5686183, DOI: 10.2139/SSRN.5686183
  3. Baba, A. (2025). Enhancing neural rehabilitation insights: on the path of bridging artificial and biological neural networks. Brain Informatics, 12.
  4. Baba, A. (2025). A New Technique Integrating Adam Optimizer and Q-Learning Algorithm for Adaptive Neurofeedback Therapy
  5. Baba, D., Alothman, B., Shabon, M., & Salem, Z. (2024). Design Considerations for Hybrid Underwater Target Tracking: Integrating Particle Filters and Deep Learning for Enhanced Decision. 2024 International Conference on Decision Aid Sciences and Applications (DASA), 1-5.
  6. Baba, D., & Alothman, B. (2024). Hybrid Underwater Target Tracking with Particle Filters and Deep Learning; Focus on Design Considerations. 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS), 1-6.
  7. Baba, D., & Alothman, B. (2024). Safeguarding a flying robot, designed to investigate the state of Smart Grid, against cyberattacks. 2024 20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), 1-6.
  8. Baba, D. (2023). Neural Networks from Biological to Artificial and Vice Versa. Bio Systems, 105110 .
  9. Baba, D., Alothman, B., & Khattab, O. (2023). Blockchain-Based Heuristic Study to Secure UAVs From GPS Spoofing Signals and External Attacks. 2023 Fifth International Conference on Blockchain Computing and Applications (BCCA), 377-379.
  10. Baba, D., & Bonny, T. (2023). FPGA-based parallel implementation to classify Hyperspectral images by using a Convolutional Neural Network. Integr., 92, 15-23.
  11. Baba, D. (2023). Flying robots for a smarter life. ArXiv, abs/2303.12044.
  12. Baba, D., & Alothman, B. (2023). A fuzzy logic-based stabilization system for a flying robot, with an embedded energy harvester and a visual decision-making system. ArXiv, abs/2301.11225.
  13. Baba, D. (2022). Electricity-consuming forecasting by using a self-tuned ANN-based adaptable predictor. Electric Power Systems Research.
  14. Baba, D., Al Shehabi, S., & Bonny, M.T. (2022). Smart prospects for solar-based cooling and heating systems in the Middle East and Turkey. 2022 4th Global Power, Energy and Communication Conference (GPECOM), 433-438.
  15. Baba, D. (2021). Advanced AI-based techniques to predict daily energy consumption: A case study. Expert Syst. Appl., 184, 115508.
  16. Baba, D. (2020). Iris segmentation techniques to recognize the behavior of a vigilant driver. 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), 1-5.
  17. Baba, D. (2020). A new design of a flying robot, with advanced computer vision techniques to perform self-maintenance of smart grids. J. King Saud Univ. Comput. Inf. Sci., 34, 2252-2261.
  18. A. M. A. Ghiet and A. Baba, “Robot Arm Control with Arduino,” ResearchGate, Jun. 2017, doi:10.13140/RG.2.2.10227.53286.
  19. Baba, A. (2023). Probabilistic-Based Forecasting Method For Time Series Datasets. Duzce University Journal of Science and Technology, 11(2), 563-573. https://doi.org/10.29130/dubited.1022265
  20. Şahin, S., Baba, D., & Sönmez, T. (2017). Optimal fusion of multiple GNSS signals against spoofing sources. Turkish J. Electr. Eng. Comput. Sci., 25, 3289-3299.

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