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International Conference on AI-Driven Biosystem Modeling and Engineering

📅 9–10 Feb 2027 📍 Port Louis, Mauritius 👤 Standard / Listener

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$165

virtual · $165 in person

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Conference session tracks

Key research areas covered across the sessions — tap a track to read more.

This track focuses on the application of AI techniques in predictive modeling for biotechnological processes. Researchers are invited to present innovative methodologies that enhance predictive accuracy and operational efficiency.

This session will explore the integration of deep learning algorithms in the engineering of biosystems. Contributions should highlight novel approaches to data analysis and feature extraction in complex biological datasets.

This track addresses the challenges and solutions related to anomaly detection in biotechnological applications. Papers should discuss advanced techniques for identifying and mitigating anomalies in biosystem operations.

This session emphasizes the role of automation in streamlining workflows within biotechnological research and development. Submissions should focus on AI-driven solutions that enhance productivity and reduce human error.

This track investigates the intersection of industrial IoT and predictive maintenance strategies in biosystem engineering. Researchers are encouraged to present case studies and frameworks that illustrate the benefits of IoT integration.

This session will cover the development and application of digital twin technologies in simulating biosystems. Contributions should focus on the accuracy and efficiency of digital twins in predicting system behavior and performance.

This track invites discussions on computational modeling techniques for pathway analysis in biological systems. Papers should explore how these models can enhance our understanding of metabolic and signaling pathways.

This session focuses on methodologies for process optimization in biotechnological applications using AI. Researchers are encouraged to share innovative strategies that lead to improved yield and efficiency.

This track examines the challenges and solutions associated with system integration in AI-driven biosystems. Contributions should highlight interdisciplinary approaches that facilitate seamless integration of various technologies.

This session will explore the role of simulation analytics in enhancing biosystem engineering practices. Papers should focus on the application of analytical techniques to improve decision-making and system performance.

This track addresses the importance of model evaluation in the context of AI applications in biotechnology. Researchers are invited to propose new metrics and frameworks that ensure the reliability and validity of AI-driven models.