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Engineering

Information Engineering at IgMin Research | Engineering Group

Our mission is to foster interdisciplinary dialogue and accelerate the advancement of knowledge across a wide spectrum of scientific domains.

About

Information Engineering is a pivotal field that intersects technology and data management, driving innovations in our digital world. At IgMin Research, we explore the intricate web of Information Engineering, uncovering its significance and applications across various sectors. With a commitment to advancing knowledge and fostering collaborative research, our multidisciplinary journal delves into the methodologies, theories, and technologies that shape the way we manage, analyze, and utilize information in today's fast-paced society.

  • Data Modeling and Database Design
  • Information Retrieval and Storage
  • Data Privacy and Security
  • Knowledge Management Systems
  • Semantic Web and Ontology
  • Machine Learning for Information Processing
  • Natural Language Processing
  • Information Visualization
  • Big Data Analytics
  • Cloud Computing and Information Services
  • Decision Support Systems
  • Human-Computer Interaction
  • Information Architecture
  • Web Technologies and Services
  • Data Mining Techniques
  • Digital Transformation
  • Information System Development
  • Business Intelligence
  • Information Flow in Social Networks
  • Information Ethics and Policy
  • Information Quality and Validation
  • Cyber-Physical Systems
  • Computational Intelligence
  • Information Innovation and Entrepreneurship
  • Emerging Trends in Information Engineering

Engineering Group (1)

Research Article Article ID: igmin140

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Enhancing Missing Values Imputation through Transformer-Based Predictive Modeling
by Hina Ayub and Harun Jamil

This paper tackles the vital issue of missing value imputation in data preprocessing, where traditional techniques like zero, mean, and KNN imputation fall short in capturing intricate data relationships. This often results in suboptimal outcomes, and discarding records with missing values leads to significant informat...ion loss. Our innovative approach leverages advanced transformer models renowned for handling sequential data. The proposed predictive framework trains a transformer model to predict missing values, yielding a marked improvement in imputation accuracy. Comparative analysis against traditional methods—zero, mean, and KNN imputation—consistently favors our transformer model. Importantly, LSTM validation further underscores the superior performance of our approach. In hourly data, our model achieves a remarkable R2 score of 0.96, surpassing KNN imputation by 0.195. For daily data, the R2 score of 0.806 outperforms KNN imputation by 0.015 and exhibits a notable superiority of 0.25 over mean imputation. Additionally, in monthly data, the proposed model’s R2 score of 0.796 excels, showcasing a significant improvement of 0.1 over mean imputation. These compelling results highlight the proposed model’s ability to capture underlying patterns, offering valuable insights for enhancing missing values imputation in data analyses.

Information Engineering Artificial IntelligenceSensors