Registration summary
ConferenceICEAIE
ModeStandard / Physical
ParticipationListener
Registration fee$135.00
Bank charges (5.8%)$7.83
Total payable$142.83
Includes all bank processing charges — the amount above is exactly what will be charged.
Benefits of Registering as Listener
Access to Conference Sessions
Networking Opportunities
Certificate of Participation
Invitation Letter Support
Conference Kit / Materials
Access to Keynote Sessions
• Conference Session Tracks •
SDG-Aligned Research Themes
ICEAIE conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.
01 Advancements in Explainable AI for Predictive Maintenance +
This track focuses on the integration of explainable AI techniques in predictive maintenance applications within engineering contexts. Participants will explore methodologies that enhance model interpretability and transparency in maintenance decision-making processes.
02 Interpretable Models in Supervised Learning +
This session will delve into the development and application of interpretable models in supervised learning frameworks. Researchers will present novel approaches that balance model accuracy with the need for transparency and understanding.
03 Unsupervised Learning and Anomaly Detection +
This track addresses the challenges and innovations in unsupervised learning techniques for anomaly detection in engineering systems. Discussions will center on the interpretability of models and their practical implications in real-world scenarios.
04 Feature Importance and Model Evaluation Techniques +
This session will explore various methods for assessing feature importance in machine learning models. Participants will discuss the implications of these techniques on model evaluation and their role in enhancing explainability.
05 Deep Learning Interpretability Frameworks +
This track focuses on the latest frameworks and methodologies developed to enhance the interpretability of deep learning models. Researchers will share insights on bridging the gap between complex model architectures and human comprehension.
06 Human-in-the-Loop AI Systems +
This session will investigate the role of human-in-the-loop approaches in the development of explainable AI systems. Emphasis will be placed on how human feedback can improve model transparency and trustworthiness.
07 Explainable AI in Industrial IoT Applications +
This track will examine the application of explainable AI methodologies in the context of industrial IoT. Participants will discuss case studies that highlight the importance of model interpretability in enhancing operational efficiency and safety.
08 Decision Support Systems Leveraging Explainable AI +
This session will explore the integration of explainable AI in decision support systems across various engineering domains. The focus will be on how interpretability can enhance user trust and facilitate better decision-making.
09 Challenges in AI Trustworthiness and Transparency +
This track will address the critical challenges surrounding AI trustworthiness and model transparency in engineering applications. Participants will engage in discussions about ethical considerations and the societal implications of AI deployment.
10 Feature Extraction Techniques for Explainable Models +
This session will focus on innovative feature extraction techniques that enhance the interpretability of machine learning models. Researchers will present their findings on how effective feature selection contributes to model clarity and performance.
11 Case Studies in Explainable Predictive Modeling +
This track will showcase case studies that highlight the practical applications of explainable predictive modeling in engineering. Participants will analyze real-world examples where interpretability has led to improved outcomes and insights.
