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.