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 9 — Industry, Innovation and Infrastructure
SDG 11 — Sustainable Cities and Communities
SDG 12 — Responsible Consumption and Production
Explore
All Session Tracks
Track 01
Innovative Data Cleaning Techniques
This track focuses on novel methodologies for data cleaning that enhance the quality of datasets. Researchers are encouraged to present their findings on automated and semi-automated approaches to data cleansing.
Track 02
Advanced Preprocessing Strategies for Machine Learning
This session will explore sophisticated preprocessing techniques that improve the performance of machine learning models. Topics may include normalization, transformation, and feature extraction.
Track 03
Predictive Modeling in Data Science
This track aims to discuss the latest advancements in predictive modeling techniques within data science. Contributions should highlight the application of supervised and unsupervised learning methods.
Track 04
Anomaly Detection in Engineering Datasets
This session will delve into innovative approaches for detecting anomalies in engineering datasets. Researchers are invited to share their methodologies and case studies on outlier detection.
Track 05
Handling Missing Data: Techniques and Applications
This track will cover various strategies for missing data imputation and their implications for data integrity. Presentations should focus on both theoretical frameworks and practical applications.
Track 06
Data Integration for Enhanced Decision Making
This session will address the challenges and solutions related to data integration from multiple sources. Emphasis will be placed on techniques that support data-driven decision making in engineering contexts.
Track 07
Feature Engineering for Predictive Maintenance
This track will explore the role of feature engineering in predictive maintenance applications. Participants are encouraged to present innovative features that enhance model accuracy and reliability.
Track 08
Statistical Preprocessing Methods in Data Science
This session will focus on statistical approaches to preprocessing data for analysis. Contributions should discuss the impact of these methods on data quality and model performance.
Track 09
Deep Learning Techniques for Data Preprocessing
This track will investigate the application of deep learning methods in data preprocessing tasks. Researchers are invited to present their findings on how deep learning can automate and improve preprocessing workflows.
Track 10
Data Quality Assessment Frameworks
This session will examine frameworks and methodologies for assessing data quality in engineering datasets. Presentations should focus on metrics, evaluation techniques, and case studies.
Track 11
Industrial IoT and Data Cleaning Challenges
This track will address the unique challenges of data cleaning in the context of Industrial IoT. Researchers are encouraged to share insights on preprocessing techniques tailored for IoT-generated data.