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Optimization of Deep Learning in the Perspective of Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 8214

Special Issue Editors


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Guest Editor
Division of Data Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon-do 26493, Republic of Korea
Interests: deep learning; big data; artificial intelligence; image recognition; financial statistics
Special Issues, Collections and Topics in MDPI journals
Department, Business School, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea
Interests: data mining; machine/deep learning; big data analysis in healthcare
Special Issues, Collections and Topics in MDPI journals
Department of Bigdata Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Asan 31538, Chungnam, Korea
Interests: big data; machine learning; artificial intelligence; business analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of novel technologies such as mobile technology, IOT, and sensors due to the advancement of ICT technology, organizations are now faced with an environment where they create and use huge amounts of various types of big data.

While traditional data analysis focused on hypothesis verification based on deriving correlations between structured data, in the era of big data, the procedure of discovering and verifying hypotheses, taking into account not only structured data, but also unstructured data, is becoming more common.

Artificial intelligence is drawing attention as the most useful tool for such big data analyses, and, in particular, novel technology based on deep learning is being developed at a rapid pace. Novel deep learning technologies can be applied to various fields, providing key solutions to problems that humans cannot solve, and are recognized as important in academia and industry.

Therefore, deep learning technology has become an essential element in securing sustainable competitiveness for various stakeholders, and is an interesting subject to many researchers.

This Special Issue covers an approach to creative analytical models and generating innovative results through a big data analysis based on the optimization of deep learning technologies.

Topics of interest for this Special Issue include (but are not limited to):

- Artificial intelligence for sustainability;

- Business intelligence;

- Customer big data analytics;

- Deep learning approach in healthcare;

- Deep learning for financial market prediction;

- Deep learning application for text mining;

- Image reconstruction technology with deep learning;

- Novel methods for big data analytics;

- Optimization of deep learning for sustainability.

We look forward to receiving your contributions.

Dr. Jae Joon Ahn
Dr. Sukjun Lee
Dr. Jaeyun Kim
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. Sustainability 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

  • artificial intelligence
  • big data analytics
  • deep learning
  • machine learning
  • sustainability
  • text mining

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Published Papers (3 papers)

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Research

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16 pages, 3960 KiB  
Article
Sustainable Financial Fraud Detection Using Garra Rufa Fish Optimization Algorithm with Ensemble Deep Learning
by Mashael Maashi, Bayan Alabduallah and Fadoua Kouki
Sustainability 2023, 15(18), 13301; https://doi.org/10.3390/su151813301 - 5 Sep 2023
Cited by 14 | Viewed by 2536
Abstract
Sustainable financial fraud detection (FD) comprises the use of sustainable and ethical practices in the detection of fraudulent activities in the financial sector. Credit card fraud (CCF) has dramatically increased with the advances in communication technology and e-commerce systems. Recently, deep learning (DL) [...] Read more.
Sustainable financial fraud detection (FD) comprises the use of sustainable and ethical practices in the detection of fraudulent activities in the financial sector. Credit card fraud (CCF) has dramatically increased with the advances in communication technology and e-commerce systems. Recently, deep learning (DL) and machine learning (ML) algorithms have been employed in CCF detection due to their features’ capability of building a powerful tool to find fraudulent transactions. With this motivation, this article focuses on designing an intelligent credit card fraud detection and classification system using the Garra Rufa Fish optimization algorithm with an ensemble-learning (CCFDC-GRFOEL) model. The CCFDC-GRFOEL model determines the presence of fraudulent and non-fraudulent credit card transactions via feature subset selection and an ensemble-learning process. To achieve this, the presented CCFDC-GRFOEL method derives a new GRFO-based feature subset selection (GRFO-FSS) approach for selecting a set of features. An ensemble-learning process, comprising an extreme learning machine (ELM), bidirectional long short-term memory (BiLSTM), and autoencoder (AE), is used for the detection of fraud transactions. Finally, the pelican optimization algorithm (POA) is used for parameter tuning of the three classifiers. The design of the GRFO-based feature selection and POA-based hyperparameter tuning of the ensemble models demonstrates the novelty of the work. The simulation results of the CCFDC-GRFOEL technique are tested on the credit card transaction dataset from the Kaggle repository and the results demonstrate the superiority of the CCFDC-GRFOEL technique over other existing approaches. Full article
(This article belongs to the Special Issue Optimization of Deep Learning in the Perspective of Sustainability)
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16 pages, 2487 KiB  
Article
Use of Internet-of-Things for Sustainable Art Businesses: Action Research on Smart Omni-Channel Service
by Arum Park
Sustainability 2023, 15(15), 12035; https://doi.org/10.3390/su151512035 - 6 Aug 2023
Cited by 3 | Viewed by 1972
Abstract
Performance and exhibition venues are well-suited for implementing business models that leverage Internet-of-Things (IoT) concepts to integrate both online and offline information. The use of IoT technologies has led to the emergence of omni-channel services, providing customers with information from both online and [...] Read more.
Performance and exhibition venues are well-suited for implementing business models that leverage Internet-of-Things (IoT) concepts to integrate both online and offline information. The use of IoT technologies has led to the emergence of omni-channel services, providing customers with information from both online and offline channels. However, there is a lack of field research on the practical implications of IoT concepts in art exhibitions. An action research methodology is required to address this gap, and one potential solution is to implement an IoT-based omni-channel service that integrates online and offline channels to enhance service quality and provide a seamless customer experience. Near Field Communication (NFC), iBeacon, and Internet buttons can be used in an art gallery to achieve these objectives. This study utilized action research methods to examine the impact of customers’ technology interactions in an art gallery setting. The findings indicate that the inclusion of a physical interactive element has a positive effect on IoT use. Full article
(This article belongs to the Special Issue Optimization of Deep Learning in the Perspective of Sustainability)
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Other

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11 pages, 5473 KiB  
Brief Report
Design of a Nuclear Monitoring System Based on a Multi-Sensor Network and Artificial Intelligence Algorithm
by Min Kyu Baek, Yoon Soo Chung, Seongyeon Lee, Insoo Kang, Jae Joon Ahn and Yong Hyun Chung
Sustainability 2023, 15(7), 5915; https://doi.org/10.3390/su15075915 - 29 Mar 2023
Cited by 6 | Viewed by 2874
Abstract
Nuclear power is a sustainable energy source, but radiation management is required for its safe use. Radiation-detection technology has been developed for the safe management of radioactive materials in nuclear facilities but its performance may vary depending on the size and complexity of [...] Read more.
Nuclear power is a sustainable energy source, but radiation management is required for its safe use. Radiation-detection technology has been developed for the safe management of radioactive materials in nuclear facilities but its performance may vary depending on the size and complexity of the structure of nuclear facilities. In this study, a nuclear monitoring system using a multi-sensor network was designed to monitor radioactive materials in a large nuclear facility. Additionally, an artificial-intelligence-based localization algorithm was developed to accurately locate radioactive materials. The system parameters were optimized using the Geant4 Application for Tomographic emission (GATE) toolkit, and the localization algorithm was developed based on the performance evaluation of the Artificial Neural Network (ANN) and Decision Tree (D-Tree) models. In this article, we present the feasibility of the proposed monitoring system by converging the radiation detection system and artificial intelligence technology. Full article
(This article belongs to the Special Issue Optimization of Deep Learning in the Perspective of Sustainability)
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