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Sensor Systems Empowered by AI, Big Data Processing, and Platform Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 908

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, University of Alaska Anchorage, Anchorage, AK 99508, USA
Interests: big data; cyber security; machine learning; cloud computing; system architectures
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: computer vision; human–computer interaction; biometrics; medical image processing and understanding; artificial intelligence; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade, numerous countries have adopted a roadmap for develo** future infrastructure in emerging Platform Technology (PT) areas, such as automobiles, big data processing technology, biotechnology, nanotechnology, grid computing, and Information and Communication Technology (ICT), etc.

Recent progress in Deep Learning and Machine Learning has made Artificial Intelligence (AI) into a powerful tool that can be leveraged across a wide number of industries. In recognition of the significant convergence and synergies between AI, Big Data Processing (BDP), and PT, this Special Issue aims to delve into how integrated applications can pave the way for breakthroughs and innovations in the field of sensors.

We encourage researchers to address the challenges and opportunities arising from the seamless integration of AI, big data processing, and platform technology, offering insights into how their collective contributions can shape the future landscape of technology-driven industries, especially in the realm of sensors.

Dr. Kamran Siddique
Prof. Dr. Ka Lok Man
Dr. Rizwan Ali Naqvi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Sensors 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 2600 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

  • sensor technology
  • artificial intelligence
  • big data processing
  • big data applications
  • platform technology
  • data science
  • emerging AI
  • sensor systems

Published Papers (1 paper)

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Research

16 pages, 1920 KiB  
Article
Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning
by Khurram Shabbir, Muhammad Umair, Sung-Han Sim, Usman Ali and Mohamed Noureldin
Sensors 2024, 24(13), 4218; https://doi.org/10.3390/s24134218 - 28 Jun 2024
Viewed by 370
Abstract
This study utilizes artificial neural networks (ANN) to estimate prediction intervals (PI) for seismic performance assessment of buildings subjected to long-term ground motion. To address the uncertainty quantification in structural health monitoring (SHM), the quality-driven lower upper bound estimation (QD-LUBE) has been opted [...] Read more.
This study utilizes artificial neural networks (ANN) to estimate prediction intervals (PI) for seismic performance assessment of buildings subjected to long-term ground motion. To address the uncertainty quantification in structural health monitoring (SHM), the quality-driven lower upper bound estimation (QD-LUBE) has been opted for global probabilistic assessment of damage at local and global levels, unlike traditional methods. A distribution-free machine learning model has been adopted for enhanced reliability in quantifying uncertainty and ensuring robustness in post-earthquake probabilistic assessments and early warning systems. The distribution-free machine learning model is capable of quantifying uncertainty with high accuracy as compared to previous methods such as the bootstrap method, etc. This research demonstrates the efficacy of the QD-LUBE method in complex seismic risk assessment scenarios, thereby contributing significant enhancement in building resilience and disaster management strategies. This study also validates the findings through fragility curve analysis, offering comprehensive insights into structural damage assessment and mitigation strategies. Full article
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