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Innovative Applications of Big Data and Cloud Computing, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 716

Special Issue Editors


E-Mail Website1 Website2
Guest Editor
Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan
Interests: cloud computing; big data; smart IoT; parallel and distributed processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Interests: cloud computing; big data; web-based applications; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Humans are constantly generating huge amounts of data in different situations, e.g., living, manufacturing, research, etc. In recent years, the capture and processing of data have become easier, with applications designed to assist us in making decisions. For example, the air quality index (AQI) represents the pollution degree, and scientists collect the AQI value to offer constant suggestions for outdoor activities. Analysts analyze traffic data to discover transportation demands, and drivers plan their travels through road usage. Production managers use manufacturing data to ensure that product quality remains within acceptable tolerance ranges.

Such innovation services require handling of mass data to derive suggestions. Cloud computing has been of great assistance in allowing data to be controlled easily and efficiently. The on-demand delivery of services is the major advantage of the cloud computing, allowing services to be easily invoked without any hardware or software limitations nor geographic considerations. Thus, information delivery and data analysis can be separated, and analysts and researchers can focus on the system purposes. Both system designers and users prefer to access systems via cloud services.

The following Special Issue coincides with the 7th International Symposium on Computer, Consumer, and Control (IS3C2025), to be held on 27–30 June 2025 in Taiwan. The symposium provides a global forum for international participants to share their knowledge and latest research and ideas within the field of computer, consumer, and control. The theme of IS3C 2025 covers computers, multimedia and intelligence, communication applications, integrated circuits, consumer electronics, renewable energy, systems and control, and digital signal processing, which can all form part of this Special Issue.

This conference will provide an opportunity for colleagues to communicate and exchange scientific and technological work in the field of information and communication applications, promote innovation in the discipline, and inspire and boost scientific research and applications. Renowned scientists, both at home and abroad, are invited to deliver keynote speeches on the recent advancements and frontiers in the relevant fields.

To explore the innovation service and practical systems, the Special Issue “Innovative Applications of Big Data and Cloud Computing, 2nd Edition” aims to explore the application of core service design, platform implementation, data visualization, and future prediction using big data and cloud computing. We invite researchers to contribute their state-of-the-art experimental or computational results, and the topics of particular interest are as follows:

  • Cloud system design and implementation.
  • Core service design and implementation in cloud or web ecosystems.
  • Front-end service design and implementation in cloud or web ecosystems.
  • Big data analysis and implementation in cloud or web ecosystems.
  • Big data visualization in cloud or web ecosystems.

Please feel free to contact us with any questions.

Prof. Dr. Chao-Tung Yang
Prof. Dr. Chen-Kun Tsung
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 Sciences 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 2400 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

  • big data
  • cloud computing
  • innovation service design
  • practical platform implementation

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Published Papers (1 paper)

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Research

26 pages, 2297 KiB  
Article
An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
by Fabio Cassano, Anna Maria Crespino, Mariangela Lazoi, Giorgia Specchia and Alessandra Spennato
Appl. Sci. 2025, 15(7), 4013; https://doi.org/10.3390/app15074013 - 5 Apr 2025
Viewed by 372
Abstract
Early warning systems (EWSs) are crucial for optimising predictive maintenance strategies, especially in the industrial sector, where machine failures often cause significant downtime and economic losses. This research details the creation and evaluation of an EWS that incorporates deep learning methods, particularly using [...] Read more.
Early warning systems (EWSs) are crucial for optimising predictive maintenance strategies, especially in the industrial sector, where machine failures often cause significant downtime and economic losses. This research details the creation and evaluation of an EWS that incorporates deep learning methods, particularly using Long Short-Term Memory (LSTM) networks enhanced with attention layers to predict critical machine faults. The proposed system is designed to process time-series data collected from an industrial printing machine’s embosser component, identifying error patterns that could lead to operational disruptions. The dataset was preprocessed through feature selection, normalisation, and time-series transformation. A multi-model classification strategy was adopted, with each LSTM-based model trained to detect a specific class of frequent errors. Experimental results show that the system can predict failure events up to 10 time units in advance, with the best-performing model achieving an AUROC of 0.93 and recall above 90%. Results indicate that the proposed approach successfully predicts failure events, demonstrating the potential of EWSs powered by deep learning for enhancing predictive maintenance strategies. By integrating artificial intelligence with real-time monitoring, this study highlights how intelligent EWSs can improve industrial efficiency, reduce unplanned downtime, and optimise maintenance operations. Full article
(This article belongs to the Special Issue Innovative Applications of Big Data and Cloud Computing, 2nd Edition)
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