Call for papers/Topics

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

1. Core Computational Intelligence

This track covers the core algorithmic frameworks that provide the “brains” for modern engineering systems.

• Neural Networks and Deep Learning
  • Physics-Informed Neural Networks (PINNs) for modeling physical systems
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for time-series forecasting
  • Transformers and Large Language Models (LLMs) for industrial documentation and code generation
• Evolutionary Computation and Swarm Intelligence
  • Genetic Algorithms (GA) for multi-objective optimization
  • Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)
  • Differential Evolution (DE) for complex engineering design spaces
• Fuzzy Logic and Multi-Criteria Decision-Making
  • Fuzzy control systems for handling operational uncertainty
  • Type-2 fuzzy systems for highly non-linear environments
  • Rough sets and granular computing
• Reinforcement Learning (RL)
  • Deep Q-Networks (DQN) for sequential decision-making
  • Actor-Critic models for continuous control in robotics and machinery
  • Multi-agent reinforcement learning (MARL) for decentralized systems

2. Energy Engineering

This track covers the physical infrastructure, thermodynamic principles, and generation mechanisms of energy systems.

• Renewable Energy Generation
  • Photovoltaic (PV) cell physics, tracking systems, and materials
  • Wind turbine aerodynamics, offshore structures, and mechanical drivetrains
  • Bioenergy, green hydrogen production, and geothermal systems
• Power Systems and Grid Infrastructure
  • High-Voltage Direct Current (HVDC) transmission
  • Substation automation and protection relays
  • Grid stability, power quality, and reactive power compensation
• Energy Storage Technologies
  • Lithium-ion, solid-state, and flow battery chemistry
  • Pumped-storage hydropower and compressed air energy storage (CAES)
  • Thermal energy storage (TES) for industrial processes
• Thermal and Fluid Systems
  • Advanced thermodynamics and heat exchanger design
  • Carbon Capture, Utilization, and Storage (CCUS) technologies
  • Co-generation and poly-generation plants

3. Industrial Engineering

This track focuses on the optimization of complex processes, logistics, human systems, and manufacturing operations.

• Operations Research and Optimization
  • Linear, integer, and non-linear programming
  • Stochastic modeling and queuing theory
  • Network flow models and graph theory applications
• Production Planning and Control
  • Material Requirements Planning (MRP) and Enterprise Resource Planning (ERP)
  • Just-In-Time (JIT) and Lean manufacturing methodologies
  • Shop-floor scheduling and assembly line balancing
• Supply Chain and Logistics Management
  • Facility location selection and warehouse layout design
  • Vehicle Routing Problems (VRP) and fleet management
  • Inventory control theory and global logistics network design
• Quality Engineering and Reliability
  • Six Sigma, Statistical Process Control (SPC), and Total Quality Management (TQM)
  • Failure Mode and Effects Analysis (FMEA)
  • Life-cycle analysis and system reliability modeling

4. Computational Intelligence + Energy Engineering (Interrelated)

This intersection focuses on using smart algorithms to manage the volatility, physics, and deployment of energy assets.

• Smart Grid Analytics and Forecasting
  • AI-driven net-load forecasting (combining load and behind-the-meter solar)
  • Deep learning for ultra-short-term wind power ramp forecasting
  • Fuzzy-logic-based state estimation in distribution grids
• Intelligent Energy Storage Management
  • Reinforcement learning for optimal battery charging/discharging cycles
  • Neural network estimators for State of Health (SoH) and State of Charge (SoC)
  • Machine learning for battery thermal runaway prediction
• Microgrid and Virtual Power Plant (VPP) Control
  • Multi-agent systems for decentralized microgrid energy balancing
  • Genetic algorithms for optimal sizing and placement of distributed energy resources
  • Deep RL for real-time bidding strategies of VPPs in electricity markets

5. Computational Intelligence + Industrial Engineering (Interrelated)

This intersection deals with embedding intelligence into factories, supply chains, and operational workflows (Industry 4.0/5.0).

• Smart Manufacturing and Cyber-Physical Systems
  • Computer vision for real-time automated quality inspection on assembly lines
  • Reinforcement learning for dynamic, real-time job-shop scheduling
  • Digital Twins powered by real-time machine learning data streams
• Intelligent Logistics and Supply Chain AI
  • Heuristics and swarm intelligence for solving dynamic Vehicle Routing Problems
  • Machine learning for demand sensing and predictive inventory management
  • Natural Language Processing (NLP) for automated supplier contract analysis
• Predictive Maintenance and Diagnostics
  • Deep learning anomalies detection in vibration, acoustic, and thermal sensor data
  • Survival analysis and machine learning for Remaining Useful Life (RUL) estimation
  • Fuzzy expert systems for root-cause failure diagnostics

6. Energy Engineering + Industrial Engineering (Interrelated)

This intersection focuses on the operational efficiency, sustainability, and management of energy within industrial plants.

• Industrial Energy Management Systems (IEMS)
  • Waste heat recovery optimization in manufacturing plants
  • Energy-conscious scheduling of high-power industrial machinery
  • Peak-shaving and load-shifting strategies for manufacturing facilities
• Sustainable Supply Chains and Circular Economy
  • Green logistics and carbon-footprint minimization in freight transport
  • Reverse logistics for recycling, remanufacturing, and battery second-life processing
  • Life Cycle Assessment (LCA) integrated into product design workflows
• Facility Energy Efficiency
  • HVAC optimization for large-scale industrial warehouses and cleanrooms
  • Co-optimization of water and energy networks in process industries (Pinch Analysis)

7. The Three-Way Nexus: CI + Energy + Industrial Engineering

This represents the ultimate convergence, where intelligent algorithms optimize industrial operations precisely around energy constraints, market prices, and sustainability targets.

• Demand Response Optimization
  • Using reinforcement learning to automatically adjust a factory’s production schedule based on real-time electricity pricing and grid stress signals.
• Smart Asset Lifecycle and Operations Management
  • Combining industrial reliability engineering with energy asset physics and CI algorithms to schedule predictive maintenance for wind farms or solar arrays, minimizing downtime during peak generation windows.
• Decarbonized Industrial Cluster Co-Optimization
  • Deploying swarm intelligence and multi-agent RL to coordinate energy generation, carbon capture systems, hydrogen production, and manufacturing output across an entire localized industrial park.