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

Website https://ureng.urst.org/conference/178 | Edit Freely

Category AI, Electrical & Electronics Engineering

Deadline: July 01, 2026 | Date: July 20, 2026-July 22, 2026

Venue/Country: Paris, France

Updated: 2026-01-28 18:11:17 (GMT+9)

Call For Papers - CFP

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.