Mathematical Modeling and Artificial Intelligence in Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1356

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Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
Interests: additive manufacturing; numerical simulation; machine learning; electronic packaging; artificial intelligence; sampling algorithms
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Special Issue Information

Dear Colleagues,

We are pleased to invite submissions to the Special Issue titled "Mathematical Modeling and Artificial Intelligence in Engineering". This Special Issue seeks to advance the discourse on the integration of artificial intelligence and mathematical modeling in engineering, with an emphasis on innovative and applied research that addresses complex technical challenges.

Our aim is to create a platform where industry professionals, especially those with a wealth of practical experience but limited publication history, can share their insights and innovations. This Special Issue is an excellent opportunity for engineers and software developers to enhance their professional profiles and engage with the broader academic and industrial communities.

We encourage submissions that exemplify the state-of-the-art research and applications in AI and mathematical modeling across various engineering disciplines. Manuscripts that elucidate the practical implications of these technologies in solving real-world engineering problems are particularly welcome.

Dr. Weishen Chu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

  • artificial intelligence
  • machine learning
  • engineering applications
  • mathematical modeling
  • industrial automation
  • semiconductor engineering

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Published Papers (1 paper)

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Research

22 pages, 6749 KiB  
Article
Advances in Mathematical Models for AI-Based News Analytics
by Fahim Sufi
Mathematics 2024, 12(23), 3736; https://doi.org/10.3390/math12233736 - 27 Nov 2024
Cited by 1 | Viewed by 1115
Abstract
The exponential growth of digital news sources presents a critical challenge in efficiently processing and analyzing vast datasets to derive actionable insights. This paper introduces a GPT-based news analytics system that addresses this issue using advanced mathematical modeling and AI techniques. Over a [...] Read more.
The exponential growth of digital news sources presents a critical challenge in efficiently processing and analyzing vast datasets to derive actionable insights. This paper introduces a GPT-based news analytics system that addresses this issue using advanced mathematical modeling and AI techniques. Over a 405-day period, the system processed 1,033,864 news articles, categorizing 90.67% into 202 subcategories across 11 main categories. The system achieved an average precision of 0.924, recall of 0.920, and F1-score of 0.921 in event correlation analysis and demonstrated a fast average execution time of 21.38 s per query, enabling near-real time analysis. The system critically analyzes semantic relationships between events, allowing for robust event correlation analysis, with precision and recall reaching up to 1.000 for specific pairs such as “UFO” and “Cyber”. Using dimensional augmentation, probabilistic feature extraction, and a semantic knowledge graph, the system provides robust event relationships for modeling unstructured news reports. Additionally, the integration of spectral residual and convolutional neural networks helps to identify anomalies in time-series news data with 85% sensitivity. Unlike existing solutions reported in the literature, the proposed system introduces a unified mathematical framework for large-scale news analytics, seamlessly integrating advanced methods such as large language models, knowledge graphs, anomaly detection, and event correlation to deliver fast and efficient performance. This scientifically novel and scalable framework offers a transformative approach to solving the pressing problem of news analytics, offering significant value to researchers, policymakers, and media analysts. Full article
(This article belongs to the Special Issue Mathematical Modeling and Artificial Intelligence in Engineering)
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