ICMMDIE · Registering as Listener

International Conference on Multi-Modal Data Integration in Engineering

📅 3–4 Feb 2027 📍 Lima, Peru 👤 Standard / Listener

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$150

virtual · $150 in person

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Official invitation letterIssued automatically after registration

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Certificate & digital materialsCertificate, slides and resource materials

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Supporting global researchConnect with researchers across 30+ countries

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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 latest methodologies for integrating diverse data types in engineering applications. Emphasis will be placed on innovative approaches that enhance the efficacy of data fusion processes.

This session will explore the development and implementation of predictive models tailored for engineering challenges. Participants will discuss case studies that illustrate the impact of predictive analytics on decision-making.

This track will delve into the applications of supervised and unsupervised learning techniques in various engineering domains. Discussions will highlight their effectiveness in extracting insights from complex datasets.

This session will cover the application of deep learning algorithms in the analysis of engineering data. Participants will share their experiences and results from using deep learning to solve real-world engineering problems.

This track will focus on methodologies for detecting anomalies in industrial systems using multi-modal data. The session aims to present novel techniques that enhance operational reliability and safety.

This session will explore various feature fusion strategies that improve the quality of data analysis in engineering. Participants will discuss the challenges and solutions in integrating features from multiple data sources.

This track will examine the integration of IoT data in engineering applications to create smart solutions. Discussions will focus on the challenges of real-time data processing and analytics.

This session will highlight the role of real-time monitoring in predictive maintenance strategies. Participants will present case studies demonstrating the benefits of timely interventions in industrial settings.

This track will cover the application of machine learning techniques in optimizing engineering systems. The focus will be on practical implementations that lead to improved performance and efficiency.

This session will address the critical role of data preprocessing in achieving optimal model performance. Participants will share best practices and methodologies for preparing data for analysis.

This track will explore the development of decision support systems that leverage multi-modal data for engineering applications. The session aims to highlight how data-driven approaches can enhance strategic decision-making.