Call For Papers - The World's Largest Index/List of Call For Papers

AIEME-25

Topics of Interest for Submission include, but are Not Limited to:

I. Artificial Intelligence (AI) - Foundational and Applied:

Machine Learning (ML) & Deep Learning (DL):
- Supervised, unsupervised, reinforcement learning algorithms.
- Neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers.
- Generative AI (GenAI) for design, optimization, and content creation in engineering.
- Physics-informed AI/Machine Learning (PIML) for integrating physical laws into AI models.
- Explainable AI (XAI) and AI ethics in critical applications.

Data Science & Analytics:
- Big data management, processing, and analysis for large-scale engineering data.
- Predictive analytics, forecasting, anomaly detection.
- Data visualization and interpretation for engineers.
- Data fusion from diverse sensors and sources.

AI for Control & Automation:
- Intelligent control systems, adaptive control, optimal control.
- Robotics and autonomous systems (industrial robots, drones, autonomous vehicles in industrial settings).
- Human-robot interaction and collaboration (cobots).

AI Architectures & Platforms:
- Edge AI, fog computing, and cloud computing for industrial applications.
- AI hardware accelerators (GPUs, TPUs, neuromorphic chips).
- AI software frameworks and tools.

II. AI in Energy Systems:

Smart Grids & Energy Management:
- AI for optimizing energy generation, transmission, and distribution.
- Predictive maintenance of grid infrastructure (transformers, power lines).
- Demand forecasting and response management.
- Load balancing and peak shaving using AI.
- Microgrid optimization and control.

Renewable Energy Integration & Optimization:
- Forecasting wind power, solar irradiance, and hydropower generation.
- AI for optimal siting of renewable energy facilities.
- Hybrid renewable energy systems management.
- Energy storage optimization (batteries, hydrogen, pumped hydro).

Energy Efficiency & Sustainability:
- AI for optimizing energy consumption in industrial facilities, buildings, and transportation.
- Carbon emissions monitoring, prediction, and reduction strategies using AI.
- AI for resource management and waste reduction in energy production.

Energy Infrastructure & Security:
- Predictive maintenance for power plants, turbines, and other energy assets.
- Cybersecurity for critical energy infrastructure (SCADA systems, smart meters).
- AI for fault detection and diagnosis in complex energy systems.

New Energy Technologies:
- AI in nuclear fusion research.
- AI for advanced materials in energy (e.g., better batteries, solar cells).
- Optimization of hydrogen production, storage, and distribution.

III. AI in Manufacturing Engineering:

Smart Manufacturing & Industry 4.0/5.0:
- Digital twins and cyber-physical systems for real-time monitoring and control.
- AI for predictive maintenance of manufacturing machinery (e.g., CNC machines, assembly lines).
- Autonomous manufacturing systems and intelligent automation.
- Human-AI collaboration in production environments (Operator 5.0).

Production Optimization & Control:
- AI-driven production scheduling and resource allocation.
- Real-time process optimization and adaptive control.
- Quality control and defect detection using computer vision and sensor data.
- Root cause analysis of production issues with AI.

Advanced Manufacturing Processes:
- AI for additive manufacturing (3D printing) – design, process control, quality assurance.
- Robotics and automation in assembly, welding, material handling, and finishing.
- AI for generative design of components and products.
- Simulation and modeling of manufacturing processes using AI.

Supply Chain & Logistics:
- AI for optimizing supply chain planning, logistics, and inventory management.
- Demand forecasting and risk management in supply chains.
- Blockchain for secure and transparent supply chain operations.

Workforce & Human Factors:
- AI for skill development and training in manufacturing.
- Ergonomics and safety in AI-driven manufacturing environments.
- AI-assisted decision-making for human operators.

Sustainable Manufacturing:
- AI for reducing waste and optimizing material usage.
- Circular economy principles in manufacturing driven by AI.
- Energy efficiency in factories and production lines.

IV. Cross-Cutting & Interdisciplinary Themes:

Integration of AI, Energy, and Manufacturing: Case studies and real-world applications demonstrating synergistic benefits.

Cybersecurity for AI-enabled Systems: Protecting industrial and energy infrastructure from AI-driven threats.

Data Governance & Management: Strategies for collecting, storing, and utilizing vast amounts of data from energy and manufacturing systems.

Ethics, Regulation, and Policy: Discussing the societal implications, regulatory frameworks, and ethical considerations of AI in these critical sectors.

Digital Transformation Strategies: Roadmaps and challenges for implementing AI across organizations.

Economic Impact & ROI: Analyzing the financial benefits and investment returns of AI adoption.

Education & Workforce Development: Addressing the skills gap and preparing the next generation of engineers.