41st PARIS World Congress on Advances in AI, Electrical & Electronics Engineering: AIEES-26

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.