Conference Tracks

Track One: Artificial Intelligence and Renewable Energy

Employing AI technologies in developing sustainable energy systems by improving production efficiency, resource management, and economic feasibility analysis. This track showcases predictive models and smart applications in solar energy, wind energy, and storage contributing to enhanced reliability and reduced losses in smart grids.

Sub-tracks:

  1. Predictive models for solar and wind energy production based on climatic conditions.
  2. Optimizing energy distribution in smart grids using AI algorithms.
  3. Battery and storage system management through operational data analysis.
  4. Economic feasibility analysis of energy projects using intelligent tools.
  5. Monitoring and automatic maintenance of energy systems via machine learning.
  6. Designing hybrid energy systems combining multiple sources and adapting to demand.
  7. Developing AI-supported software for solar energy calculations.

Track Two: Artificial Intelligence in Medicine

Advancing healthcare systems by improving diagnostic accuracy, supporting therapeutic decision-making, analyzing medical images, and regulating radiation doses. This track also emphasizes medical education and highlights research applications in genomics, epidemiology, and pharmaceuticals.

Sub-tracks:

  1. AI-based disease diagnosis and treatment guidance.
  2. Tissue classification from medical images using deep learning techniques.
  3. Monitoring and maintaining medical devices through intelligent systems based on operational data.
  4. Medical image analysis for early tumor detection and enhancement of low-resolution images.
  5. Radiation dose regulation in radiotherapy by identifying tumor type and location, precise radiation distribution, and monitoring patient response.
  6. Epidemiological data analysis and predictive modeling of disease spread.
  7. Generating new pharmaceutical compounds in biochemistry using AI algorithms.
  8. Designing virtual environments to train medical staff in clinical and surgical skills.
  9. Developing smart healthcare systems to improve resource management and appointment scheduling.

Track Three: Artificial Intelligence in Engineering

Employing AI technologies in developing intelligent engineering systems by enhancing automation control, improving operational efficiency, and reducing failures in industrial environments and smart buildings.
This track also addresses the use of machine learning models in simulating thermal and mechanical conditions, and developing self-learning robots to enhance adaptability in complex engineering systems.

Sub-tracks:

  1. Regulating automation systems in factories and smart buildings using adaptive algorithms.
  2. Improving efficiency of engines and thermal systems through operational data analysis.
  3. Reducing downtime and extending equipment lifespan via predictive maintenance.
  4. Stress and heat simulation in materials and structures using precise machine learning models.
  5. Developing industrial robots based on self-learning to perform complex tasks and adapt to changing environments.

Track Four: Artificial Intelligence in Education and Training

Reshaping educational and training environments through interactive learning systems based on smart simulations and realistic scenarios that enhance understanding and practical application. This track also focuses on analyzing trainee performance and customizing educational content according to their level and behavior, supporting interdisciplinary education and improving training efficiency in academic and professional contexts.

Sub-tracks:

  1. Building interactive learning environments with AI supported simulations to embody theoretical concepts in practical contexts.
  2. Generating realistic training scenarios for students and professionals simulating actual professional challenges.
  3. Analyzing trainee performance within simulation environments and providing intelligent feedback to improve skills.
  4. Customizing training content based on learner level and behavior using adaptive learning models.
  5. Supporting interdisciplinary education through flexible simulation models customizable by field and context.