The State of the Art in Generative AI: Innovations and Applications in Engineering and Technology

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 October 2024 | Viewed by 643

Special Issue Editors


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Guest Editor
Applied Research Center, Florida International University, Miami, FL 33174, USA
Interests: artificial intelligence; quantum computing; machine learning; cyber security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Interests: quantum computing; artificial intelligence; machine learning; deep learning; big data; visualization; cybersecurity; advanced cyber analytics; memory forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue investigates the rapidly evolving field of generative artificial intelligence (AI), highlighting cutting-edge research, innovations, and the practical applications of this technology. Generative AI, a transformative force in technology and engineering, is reshaping how we approach problem solving, design, and creativity. This issue aims to explore generative AI, from its foundational algorithms to its groundbreaking applications across various industries.

The key themes of this Special Issue include the development of new generative models, improvements in algorithmic efficiency and creativity, and the ethical implications of AI-generated content. Contributions are sought that demonstrate novel uses of generative AI in areas such as autonomous system design, predictive modeling, advanced manufacturing, digital media, and healthcare innovation.

This issue will feature research articles, reviews, and case studies that not only present technological advancements, but also address the societal and ethical dimensions of these technologies. We welcome submissions that offer unique insights into the current state and future potential of generative AI, particularly those that embody interdisciplinary approaches and showcase practical applications that could lead to significant societal benefits. Through this Special Issue, we aim to provide a comprehensive overview of the generative AI landscape, inspiring further innovation and collaboration in this dynamic field.

Dr. Jayesh Soni
Dr. Upadhyay Himanshu
Guest Editors

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied System Innovation is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • generative artificial intelligence
  • algorithmic creativity
  • autonomous system design
  • predictive modeling
  • AI in advanced manufacturing
  • digital media and AI
  • ethical implications of AI
  • AI in healthcare innovation
  • interdisciplinary AI applications
  • AI-driven technological advancements

Published Papers (1 paper)

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Research

20 pages, 6045 KiB  
Article
Online Prediction Method of Transmission Line Icing Based on Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion and Improved Beluga Whale Optimization Algorithm–Least Squares Support Vector Regression
by Qiang Li, Xiao Liao, Wei Cui, Ying Wang, Hui Cao and Xianjing Zhong
Appl. Syst. Innov. 2024, 7(3), 40; https://doi.org/10.3390/asi7030040 - 16 May 2024
Viewed by 314
Abstract
Due to the prevalent challenges of inadequate accuracy, unstandardized parameters, and suboptimal efficiency with regard to icing prediction, this study introduces an innovative online method for icing prediction based on Robust STL–BTSF and IBWO–LSSVR. Firstly, this study adopts the Robust Seasonal Decomposition of [...] Read more.
Due to the prevalent challenges of inadequate accuracy, unstandardized parameters, and suboptimal efficiency with regard to icing prediction, this study introduces an innovative online method for icing prediction based on Robust STL–BTSF and IBWO–LSSVR. Firstly, this study adopts the Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion (Robust STL–BTSF) approach, which is demonstrably effective for short-term and limited sample data preprocessing. Subsequently, injecting a multi-faceted enhancement approach to the Beluga Whale Optimization algorithm (BWO), which integrates a nonlinear balancing factor, a population optimization strategy, a whale fall mechanism, and an ascendant elite learning scheme. Then, using the Improved BWO (IBWO) above to optimize the key hyperparameters of Least Squares Support Vector Regression (LSSVR), a superior offline predictive part is constructed based on this approach. In addition, an Incremental Online Learning algorithm (IOL) is imported. Integrating the two parts, the advanced online icing prediction model for transmission lines is built. Finally, simulations based on actual icing data unequivocally demonstrate that the proposed method markedly enhances both the accuracy and speed of predictions, thereby presenting a sophisticated solution for the icing prediction on the transmission lines. Full article
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