41st PARIS World Congress on Advances in AI, Electrical & Electronics Engineering (AIEES-26) scheduled on July 20-22, 2026 Paris (France)

AIEES-26


Electrochemistry Microelectronics & Electronic Packaging



Call for Papers: AIEES-26



 



All Abstracts, Reviews, short articles, Full articles, Posters are welcomed related with any of the following research fields:



1. Artificial Intelligence



These topics focus on the computational and algorithmic side of the field.





  • Machine Learning (ML) Foundations: Supervised, unsupervised, and reinforcement learning.




  • Deep Learning: Neural network architectures (CNNs, RNNs, Transformers).




  • Natural Language Processing (NLP): Sentiment analysis, LLMs, and translation.




  • Computer Vision: Image segmentation, object detection, and facial recognition.




  • AI Ethics & Governance: Bias mitigation, explainability (XAI), and safety protocols.





2. Electrical & Electronics Engineering



These represent the core physical and mathematical foundations of EEE.





  • Circuit Theory & Analysis: KCL/KVL, AC/DC analysis, and network theorems.




  • Semiconductor Devices: Diodes, MOSFETs, BJTs, and FinFETs.




  • Power Systems: Generation, transmission, distribution, and smart grids.




  • Control Systems: Linear system theory, PID controllers, and feedback loops.




  • Digital Electronics: Logic gates, FPGA design, and Microprocessors/Microcontrollers.




  • Electromagnetics: Maxwell’s equations, wave propagation, and antenna design.





3. The Intersection



This is where AI algorithms meet physical hardware and electrical energy.



A. Intelligent Power & Energy Systems





  • Smart Grid Optimization: Using AI to predict load demand and manage distributed energy resources.




  • Predictive Maintenance: Using ML to analyze vibration and thermal data to predict transformer or motor failure.




  • Renewable Energy Forecasting: Neural networks used to predict solar irradiance and wind speeds.





B. Embedded AI & Hardware Acceleration





  • TinyML: Deploying ultra-low-power ML models on microcontrollers.




  • AI Hardware Accelerators: Designing specialized chips (TPUs, NPUs) and CMOS circuits optimized for tensor operations.




  • Neuromorphic Engineering: Designing circuits that mimic the biological structure of the human brain.





C. Robotics & Advanced Control





  • Autonomous Systems: Merging sensor fusion (Lidar/Radar) with AI for self-driving vehicles and drones.




  • Intelligent Control: Replacing traditional PID controllers with Reinforcement Learning (RL) for complex nonlinear systems.




  • Industrial Automation (Industry 4.0): AI-driven PLC (Programmable Logic Controller) systems.





D. Signal Processing & Communication





  • AI-Driven DSP: Using deep learning for noise reduction, echo cancellation, and signal reconstruction.




  • 6G & Cognitive Radio: AI algorithms managing frequency spectrum allocation and beamforming in wireless networks.