International Conference on Cloud-Based Data Science Platforms for Engineering (ICCDBP - 26)

This track focuses on innovative predictive modeling techniques utilizing cloud-based platforms. Researchers are invited to present methodologies that enhance the accuracy and efficiency of predictive analytics in engineering applications.

This session explores the application of big data analytics in various engineering domains. Contributions should highlight novel approaches and case studies that demonstrate the impact of big data on engineering decision-making.

This track examines the use of supervised and unsupervised learning techniques in engineering contexts. Papers should discuss the effectiveness of these methods in solving real-world engineering problems.

This session is dedicated to the advancements in deep learning technologies applicable to engineering. Submissions should focus on new architectures, algorithms, and applications that address complex engineering challenges.

This track investigates methods for feature extraction and anomaly detection within cloud-based data science platforms. Presentations should emphasize techniques that improve the identification of outliers in engineering datasets.

This session highlights the role of distributed computing in optimizing data processing workflows. Contributions should explore how distributed systems can be leveraged to handle large-scale engineering data efficiently.

This track focuses on the integration of cloud analytics with Internet of Things (IoT) technologies in engineering applications. Papers should discuss the challenges and solutions for analyzing IoT-generated data in real-time.

This session addresses the importance of real-time data processing for timely decision-making in engineering fields. Submissions should present frameworks and case studies that illustrate the benefits of real-time analytics.

This track emphasizes the significance of model evaluation and the development of performance metrics in data science. Researchers are encouraged to share methodologies that ensure robust evaluation of predictive models in engineering.

This session explores the design and implementation of scalable data pipelines tailored for engineering applications. Contributions should focus on best practices and innovative solutions for managing large volumes of engineering data.

This track investigates strategies for workflow automation and optimization in cloud-based environments. Papers should discuss tools and techniques that enhance operational efficiency in engineering processes.