About The Conference

Conference

Conference Aims & Scope

International Conference on Artificial Intelligence and Smart Data for Sustainable Engineering and Innovation (AISEI 2026) AISEI 2026 aims to bring together prominent researchers, academics, industry professionals, and policymakers to delve into the latest advancements in Artificial Intelligence (AI) and Smart Data, emphasizing their applications in sustainable engineering and technological innovation. The conference will serve as a dynamic platform to exchange knowledge, share case studies, and foster interdisciplinary collaboration in areas such as smart infrastructure, clean energy systems, intelligent water and waste management, and digital transformation in engineering. With a focus on practical solutions and impactful research, AISEI 2026 welcomes high-quality contributions that address global sustainability challenges through AI-powered systems and intelligent data strategies.

AI, IoT, Smart Data, Sustainable Engineering, Engineering Innovation, Intelligent Systems, Data-Driven Solutions, Smart Cities, Machine Learning, Environmental Monitoring, Digital Transformation, Renewable Energy, Green Computing.

Conference Type: Hybrid Conference- (Online, Onsite)

Conference Plan: The AISEI-2026 conference is planned to be hosted by … ...(Attached form)... The AISEI-2026 conference is planned to be annual (every years), where it will be an interdisciplinary conference.

Paper format

  • Submitted papers, at most five pages formatted in IEEE style format.
  • The papers should be submitted online.
  • Accepted papers will be submitted for inclusion into IEEE Xplore and indexed in Scopus.

Keynote Speakers

Speaker
Comming Soon
University of xxxxx

xxxx.

Speaker
Prof. Anas Ratib Alsoud
Executive Director, Association of Arab

Universities (AAU)

Speaker
Comming Soon
University of xxxxx

xxxx.

Speaker
Comming Soon
University of xxxxx

xxxx.

Technical Tracks

  • AI-powered design optimization in green infrastructure
  • Machine learning applications in construction sustainability
  • Smart data integration for structural health monitoring
  • Intelligent decision support systems for energy-efficient buildings
  • Data-driven models for water resource management and waste management

  • AI for early warning systems in environmental hazards
  • Remote sensing data analytics for environmental quality
  • Machine learning models for air and water pollution prediction
  • Data fusion techniques for ecosystem monitoring
  • Smart sensors and AI for real-time environmental data
  • AI in climate change modeling and simulation
  • Intelligent analysis of biodiversity and natural habitats
  • AI governance in environmental risk assessment

  • AI in traffic flow optimization and congestion prediction
  • Smart mobility and autonomous transportation systems
  • IoT and AI integration in infrastructure monitoring
  • Intelligent public transportation scheduling
  • Data-driven urban logistics and route optimization
  • Smart parking systems using AI algorithms
  • AI for resilience in transport networks under stress
  • AI-enabled infrastructure safety and disaster management
  • GIS-integrated AI for spatial analysis in transportation planning

  • AI in solar and wind energy forecasting
  • Intelligent grid management using machine learning
  • Smart energy storage and consumption prediction
  • AI-based fault detection in renewable systems
  • Load balancing in hybrid power systems using AI
  • Data science applications in smart metering
  • AI in energy efficiency optimization in buildings
  • AI-driven energy demand prediction and response

  • AI tools for engineering curriculum enhancement
  • Data analytics for student performance and retention
  • Intelligent tutoring systems in technical education
  • AI in research trend prediction and publication analysis
  • Open data and reproducible research in engineering
  • Smart labs: AI-driven experimental data analysis
  • AI for research evaluation and collaboration mapping
  • Gamification and AI in engineering learning platforms

  • Ethical frameworks for responsible AI in engineering
  • Bias and fairness in data-driven engineering systems
  • Data privacy in smart infrastructure applications
  • AI regulation and policy for sustainable development
  • Cybersecurity in AI-enabled critical infrastructure
  • Societal impacts of AI in environmental governance
  • Trust and transparency in AI decision-making
  • Legal implications of AI in engineering innovations

  • AI-driven predictive maintenance in manufacturing
  • Digital twins and AI for industrial optimization
  • Smart robotics and autonomous production systems
  • AI in supply chain and logistics optimization
  • Real-time quality control using machine vision
  • Data-driven process automation in smart factories
  • AI for energy efficiency in industrial operations
  • Cyber-physical systems and AI integration
  • Smart manufacturing of nanocomposites using AI-driven quality control
  • Integration of AI with nanomaterials for intelligent product development
  • AI-driven quality control and diagnostics in semiconductor manufacturing
  • Integration of smart sensors and AI in industrial automation

  • AI applications in medical imaging diagnostics
  • Smart wearable devices for health monitoring
  • Predictive analytics for disease outbreak management
  • AI in patient-specific treatment planning
  • Data science in biomedical signal processing
  • AI for hospital resource and workflow optimization
  • Machine learning in genomics and drug discovery
  • Ethics and privacy in AI-powered healthcare

  • AI in precision agriculture and crop monitoring
  • Smart irrigation systems using data analytics
  • AI-based pest and disease prediction models
  • Supply chain optimization for agricultural products
  • Data-driven food quality and safety monitoring
  • Drone and satellite data for smart farming
  • AI in sustainable fisheries and aquaculture
  • Climate-smart agriculture using intelligent systems

  • Federated learning for decentralized AI models
  • Quantum computing and AI convergence
  • Edge AI and smart sensor networks
  • Natural Language Processing for technical applications
  • Generative AI in engineering design
  • Explainable AI for critical engineering systems
  • Multi-agent systems and AI coordination
  • Big data architectures for scalable AI deployment

  • Utilizing predictive models to improve recruitment and employee retention in engineering companies
  • Applying data-based techniques to assess staff performance
  • Optimizing workforce strategies through analytical insights
  • Data-informed decision-making for employee training and skill development in engineering settings
  • Analyzing HR data to support diversity and inclusion strategies
  • Monitoring employee satisfaction and engagement with analytical methods

  • Role of fin-tech making green finance reality
  • Green finance and fin-tech challenges
  • Fin-tech roles in facilitating sustainability and green finance
  • Blockchain, AI, and Big Data for Green Finance
  • Digital Tools for ESG Compliance and Sustainable Investing
  • How FinTech Enables Transparency in Green Bonds & Carbon Credits
  • Smart Algorithms for Renewable Energy Financing
  • FinTech’s Role in Scaling Climate-Friendly Financial Products

Co-Organizers

Organizer 1
Organizer 2
Organizer 3
Organizer 3

Co-Sponsors

Gallery

Previous conference moments

Conference Location

Jadara University, Jordan

Map

Venue Information

Jadara University Main Campus
Engineering Building, Conference Center
Irbid, Jordan

Accommodation

Special rates are available at partner hotels near the university campus. More information will be provided upon registration.

Getting There

The university is located 20km from the city center and is accessible by public transportation. Shuttle services will be provided from designated hotels to the conference venue.

Parking

Ample parking space is available at the university campus for conference attendees.