Consistent Academic Support
Science Net ensures that research activities continue without interruption in the current global situation. Participants can engage through digital and hybrid conference formats.
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Conference Program
Session Tracks
Aligned with
UN Sustainable Development Goals
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.
Why it matters
SDG 4 — Quality Education
SDG 9 — Industry, Innovation and Infrastructure
SDG 12 — Responsible Consumption and Production
Explore
All Session Tracks
Track 01
Advanced Feature Engineering Techniques
This track focuses on innovative methodologies for feature engineering that enhance the predictive power of engineering datasets. Participants will explore novel approaches to feature extraction, transformation, and selection.
Track 02
Predictive Modeling in Engineering Applications
This session delves into the development and application of predictive models tailored for engineering contexts. It emphasizes the integration of feature engineering with various modeling techniques to improve accuracy and reliability.
Track 03
Data Preprocessing Strategies for Engineering Datasets
This track examines essential data preprocessing techniques necessary for preparing engineering datasets for analysis. Topics include data cleaning, normalization, and handling missing values to ensure robust model performance.
Track 04
Supervised and Unsupervised Learning Approaches
Participants will explore the distinctions and applications of supervised and unsupervised learning in engineering. The focus will be on how feature engineering can enhance the effectiveness of these learning paradigms.
Track 05
Deep Learning and Feature Engineering Synergies
This session investigates the intersection of deep learning and feature engineering in engineering datasets. It will highlight how advanced neural network architectures can benefit from well-engineered features.
Track 06
Dimensionality Reduction Techniques
This track addresses the challenges of high-dimensional data in engineering applications through dimensionality reduction methods. Participants will discuss various algorithms and their impact on model performance and interpretability.
Track 07
Anomaly Detection in Engineering Systems
This session focuses on the role of feature engineering in enhancing anomaly detection methodologies within engineering systems. It will cover various techniques for identifying outliers and ensuring system reliability.
Track 08
Variable Selection and Model Optimization
This track explores strategies for effective variable selection and model optimization in predictive modeling. Emphasis will be placed on techniques that maximize model efficiency while minimizing complexity.
Track 09
Predictive Maintenance and Data-Driven Insights
Participants will discuss the application of feature engineering in predictive maintenance strategies for engineering systems. The focus will be on deriving actionable insights from data to enhance operational efficiency.
Track 10
IoT Analytics and Feature Engineering
This session examines the role of feature engineering in the analysis of IoT-generated data within engineering contexts. Participants will explore methods for integrating and transforming data to extract meaningful insights.
Track 11
Model Evaluation and Performance Metrics
This track addresses the critical aspects of model evaluation and the selection of appropriate performance metrics in engineering applications. Discussions will focus on how feature engineering influences model assessment and validation.