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Engineering

Artificial Intelligence 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

Artificial Intelligence (AI) is a transformative technology that has revolutionized various industries and fields, ranging from healthcare and finance to manufacturing and entertainment. At IgMin Research's STEM journal, we delve into the dynamic realm of Artificial Intelligence, exploring its theoretical foundations, practical applications, and ethical considerations. With a commitment to multidisciplinary research, we examine how AI intersects with engineering, technology, and other scientific domains, fostering innovation and knowledge exchange.

Artificial Intelligence, within the context of IgMin Research's STEM journal, covers a broad spectrum of topics that contribute to the advancement of AI-related knowledge and applications. The following 25 scopes are explored under this overarching theme:

  • Machine Learning Algorithms
  • Deep Learning Architectures
  • Natural Language Processing
  • Computer Vision
  • Data Mining and Analytics
  • Neural Networks
  • Reinforcement Learning
  • Cognitive Computing
  • Human-AI Interaction
  • Ethics and Bias in AI
  • AI in Healthcare
  • AI in Finance
  • Robotics and Automation
  • AI-driven Creativity
  • AI in Education
  • Explainable AI
  • AI Ethics and Policy
  • AI for Smart Cities
  • AI in Agriculture
  • AI in Manufacturing
  • AI in Transportation
  • AI and Climate Change
  • AI for Social Good
  • AI and Cybersecurity
  • AI-driven Drug Discovery

Engineering Group (2)

Review Article Article ID: igmin183
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Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.

New Scientific Field for Modelling Complex Dynamical Systems: The Cybernetics Artificial Intelligence (CAI)
by Peter P Groumpos

Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has ...developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. Despite the optimism and enthusiasm surrounding AI, its future may turn out to be a “catastrophic Winter” for the whole world, depending on who controls its development. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence and the Wise Anthropocentric Revolution. The text proposes specific solutions for achieving these two goals. Furthermore, the importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values.

Artificial Intelligence
Research Article Article ID: igmin140
Cite

Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.

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.

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