ROME 8th International Congress on Artificial Intelligence, Electrical & Electronics Engineering: AIE3-27

Call for papers/Topics

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

Part 1: AI & Data Engineering (Foundational & Independent AI)

Machine Learning (ML) & Statistical Learning

• Supervised Learning: Regression, classification, ensemble methods (random forests, gradient boosting).

• Unsupervised Learning: Clustering, dimensionality reduction (PCA, t-SNE), anomaly detection.

• Reinforcement Learning (RL): Markov decision processes, Q-learning, deep Q-networks (DQN), policy gradients, actor-critic models.

• Semi-Supervised & Self-Supervised Learning: Pre-training strategies, contrastive learning.

Deep Learning (DL) & Neural Architectures

• Feedforward Neural Networks: Perceptrons, multilayer perceptrons (MLPs), backpropagation, optimization algorithms (Adam, SGD).

• Convolutional Networks (CNNs): Image feature extraction, spatial convolutions, pooling strategies.

• Recurrent Neural Networks (RNNs): Sequence modeling, LSTM, GRU architectures.

• Attention Mechanisms & Transformers: Self-attention, vision transformers (ViT), large language models (LLMs).

• Generative AI: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models.

Symbolic AI & Knowledge Systems

• Knowledge Representation: Ontologies, knowledge graphs, semantic web.

• Logic & Expert Systems: Propositional and predicate logic, inference engines, rule-based reasoning.

• Probabilistic Graphical Models: Bayesian networks, Hidden Markov Models (HMMs), factor graphs.

AI Infrastructure & MLOps

• Data Engineering Pipelines: ETL processes, vector databases, feature stores.

• Model Operations: Model deployment, monitoring, drift detection, quantization, pruning.

• AI Ethics & Safety: AI alignment, bias mitigation, explainable AI (XAI), privacy-preserving machine learning.

Part 2: Electrical & Electronics Engineering (Foundational & Independent EEE)

Circuit Theory & Electronic Devices

• Analog Electronics: Operational amplifiers, active filters, precision analog circuits, noise analysis.

• Digital Electronics: Logic gates, FPGA programming, ASIC design, sequential and combinational logic.

• Semiconductor Physics: P-N junctions, MOSFET mechanics, wide-bandgap materials (GaN, SiC).

• Power Electronics: AC/DC converters, switched-mode power supplies (SMPS), inverters, gate drivers.

Power Systems & Smart Grid Technology

• Power Generation & Transmission: High-voltage AC/DC (HVDC), load flow analysis, grid stability.

• Renewable Energy Integration: Photovoltaic systems, wind turbine control, energy storage system (ESS) management.

• Smart Grids: Automated metering infrastructure (AMI), demand-side management, microgrids.

Signals, Control, & Communications

• Signal Processing: Fourier analysis, digital signal processing (DSP), adaptive filtering, wavelets.

• Control Systems Theory: Linear and non-linear control, PID controllers, state-space models, adaptive control.

• Telecommunications & RF Engineering: Electromagnetics, antenna design, wireless communication protocols (5G/6G), optical networking.

Part 3: Interrelated & Interdisciplinary Domains (AI + EEE Intersections)

Embedded AI & TinyML (Electronics + AI)

• Edge Computing: On-device AI inference without cloud dependency.

• Hardware Acceleration: NPU (Neural Processing Unit) design, Tensor Processing Units (TPUs), neuromorphic computing chips.

• Ultra-Low Power Inference: Quantized neural networks for microcontrollers (MCUs), event-driven sensing.

AI for Power Systems & Smart Energy (Power EEE + AI)

• Predictive Maintenance: Fault detection in power transformers, wind turbines, and industrial machinery using sensor fusion and ML.

• Smart Grid Optimization: AI-driven load forecasting, automated dynamic pricing, renewable generation prediction.

• Battery Energy Storage Optimization: State-of-Charge (SoC) and State-of-Health (SoH) estimation for Lithium-ion batteries via deep learning.

AI in Robotics, Automation, & Control Systems (Control EEE + AI)

• Autonomous Navigation: Simultaneous Localization and Mapping (SLAM), trajectory planning, obstacle avoidance.

• Smart Industrial Automation: AI-driven Programmable Logic Controllers (PLCs), computer-vision-guided robotic assembly.

• Reinforcement Learning in Control: Replacing standard PID controllers with adaptive RL agents for non-linear dynamic systems.

Computer Vision & Audio Processing in Signal Hardware (Signals EEE + AI)

• Sensory Signal Enhancement: Machine learning for noise cancellation, image reconstruction, and radar/lidar signal synthesis.

• Bioelectric Signal Interpretation: Machine learning for Electrocardiograms (ECG), Electromyograms (EMG), and Brain-Computer Interfaces (BCI).

AI for Microelectronics & EDA (Electronic Design Automation)

• Circuit Design Automation: AI-guided PCB routing, dynamic impedance matching, transistor sizing optimization.

• Thermal & Power Optimization: Deep learning models for predicting thermal hotspots in high-performance microprocessors.

• Yield & Defect Prediction: Machine vision for silicon wafer inspection and semiconductor manufacturing quality control.