AI and Big Data in Software Engineering

•  AI-assisted software design, development, and testing

• Big Data analytics in software project management

• Machine learning for software defect prediction and quality assurance

• Natural language processing for software requirements and documentation

•  Intelligent code completion and program synthesis

• AI in DevOps and continuous delivery pipelines

• Data-driven decision-making in software maintenance and evolution

• Scalability and performance optimization with Big Data technologies

• AI-based software architecture optimization

• Case studies of AI and Big Data in real-world SE projects, including healthcare

  information systems, biometric-enabled identity management, and cross-border

  interoperability solutions

• Ethical considerations and responsible AI in software engineering

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AI Innovations for Inclusive and Sustainable Societies: Cross-Domain Applications

• AI for Healthcare, Medical Diagnostics, and Telemedicine
• AI in Education, E-learning Platforms, and Personalized Learning
• AI for Environmental Monitoring, Climate Action, and Sustainable Agriculture
• Data Mining and Machine Learning for Decision Support
• Real-time Recommender Systems and Context-aware Services
• Image Annotation, Vision-Language Models, and Computer Vision
• Real-time Prediction for commerce, finance, and supply chain optimization
• System Optimization for energy efficiency, logistics, and smart manufacturing
• Integration of AI with Blockchain, IoT, and Virtual Reality for social good
• Ethical, explainable, and trustworthy AI frameworks

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Artificial Intelligence for Medical Education, Computer-Aided Diagnosis (CAD), and Digital Health

• AI-powered personalized learning systems in medical education
• Intelligent tutoring systems and AI-based student performance assessment
• Virtual and augmented reality (VR/AR) with AI for clinical training and simulation
• Natural language processing (NLP) and conversational AI in medical education
• Computer-aided diagnosis (CAD) for medical imaging (X-ray, CT, MRI, dermatology,
pathology, etc.)
• Deep learning and energy-based models for disease detection and classification
• Explainable AI (XAI) for diagnostic imaging and clinical decision support
• Predictive analytics for disease progression, treatment outcomes, and personalized
medicine
• AI in telemedicine and remote healthcare platforms
• AI for smart hospital management and healthcare workflow optimization
• Wearable devices, IoMT (Internet of Medical Things), and AI-driven health monitoring
• Data privacy, ethics, and fairness in AI applications for healthcare
• AI applications in genomics, precision medicine, and public health surveillance

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Advances in Natural Language Processing

• Advances in LLM architecture and training techniques
• Applications of LLM in various NLP tasks
• Development and integration of KG for enhanced information retrieval
• Novel GNN approaches for NLP
• Combining LLM, KG, and GNN to solve complex NLP problems
• Case studies and applications of LLM, KG, and GNN in industry and research
• Advances in other emerging NLP technologies and methodologies
• Low-resource methods for NLP
• Advances in Multimodal Vision Language Models for combining visual and linguistic data
• Challenges and future directions in NLP with emerging technologies

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Computer Vision and Intelligent systems

• Image processing
• Object detection, pattern classification, semantic segmentation.
• Machine learning, Deep learning, Incremental learning, Semi-Supervised learning
• Image processing based medical diagnosis.
• Computer vision-based intelligent transportation systems.
• Computer vision-based surveillance systems
• Real-time and efficient solutions for robotics
• Motion estimation, localization, path planning
• Reconstruction and mapping
• Vision based control, human system interaction.
• Perceptual strategies for human robot interaction
• Sensor calibration
• Industrial robot and autonomous vehicle
• Automated reasoning
• Knowledge Acquisition
• Knowledge-based systems
• Inductive Logic and Logic programming
• Intelligent, adaptive, and personalized user interfaces
• Conversational user interfaces and chatbots

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Generative AI in Education: Innovation, Ethics, and Equity

1. GAI Readiness in Teaching and Learning
Exploring how prepared teachers and students are to adopt and integrate GAI tools
into educational settings.
2. Ethical and Responsible Use of AI in Education
Focusing on academic integrity, ethical implications, and responsible use of GAI in
classrooms and institutions.
3. Transforming Teaching and Learning Practices
Showcasing innovative uses of GAI to enhance student engagement, personalize
learning, design materials, and diversify instructional methods.
4. Rethinking Assessment and Research
Using GAI to support assessment strategies, academic research, writing, and scholarly
publishing in education.
5. Educational Leadership, Policy, and Quality Assurance
Examining GAI’s role in shaping education policies, improving governance, and
supporting accreditation and quality standards.

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Multi-Agent AI for Social Good

• Collaborative Multi-Agent systems for disaster management and recovery.
• AI-driven resource optimization for sustainable development.
•  Multi-Agent frameworks for equitable healthcare delivery.
•  Ethical considerations and fairness in Multi-Agent AI deployments.
• Scalable Multi-Agent solutions for smart cities and urban planning.
•  Integration of Multi-Agent AI with IoT and edge computing for social impact.
  This session will provide a platform for researchers, practioners, and policymakers to
  discuss advancements, share case studies, and address challenges in leveraging Multi-Agent
  AI to create impactful, inclusive, and sustainable solutions.

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Recent Advances in Intelligent Robotics and Autonomous Systems

• Autonomous mobile robots (AMRs) and path planning
• SLAM, localization, and mapping
• Robot perception and sensor fusion
• Robotic manipulators and grasping techniques
• Human–robot collaboration and safety
• Swarm robotics and multi-agent systems
• Deep learning and reinforcement learning in robotics
• UAVs and aerial robotics systems
• Robotics in agriculture, industry, and medical applications
• Embedded AI and real-time robotic control
• Bio-inspired and soft robotics

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