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.