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Sustainability
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Machine Learning and Data Mining Techniques: Towards a Sustainable Industry
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Machine Learning and Data Mining Techniques: Towards a Sustainable Industry

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2717

Special Issue Editors

Prof. Dr. Kuo-Yi Lin

E-Mail Website
Guest Editor
Business School, Guilin University of Electronic Technology, Guilin 541004, China
Interests: machine learning; decision analysis
Prof. Dr. Kuang-Cheng Chai

E-Mail Website
Guest Editor
Business School, Guilin University of Electronic Technology, Guilin 541004, China
Interests: technical management; environmental management; health economy; financial management
Dr. Ke-Chiun Chang

E-Mail Website
Guest Editor
School of Economics and Management, Wuhan University, Wuhan, China
Interests: technology strategic management; health economy

Special Issue Information

Dear Colleagues,

The scope and purpose of this Special Issue are relate to machine learning and data science techniques in various industries. The purpose of the Issue is to relate modern governance and management engineering in complex systems. The scope includes the research of the theory and methods of the effective, economical, safe and coordinated operation of engineering projects and systems, including the basic theory of complex engineering systems, management technology, analysis and decision-making, and optimization designs such as multi-factor modeling methods and the analysis of engineering and complex operations, intelligent learning, and optimization in multi-objective decision theory. The topics of this Speciall Issue include, but are not limited to, the operation management of artificial intelligence, intelligent construction, system operation, collaborative operation management, complex engineering management and control, engineering resource overall planning theory, and the methods in engineering and complex operation management.

Machine learning and data science techniques are rapidly reshaping the strategic framework of manufacturing enterprises in all industries and are leading a paradigm shift. This latest industrial revolution provides new opportunities for sustainability, but it also brings challenges. Industrial companies are facing the challenge of transferring the concept of sustainable value into real applications, threatening the established business models, changing the processes of value creation, creating new security risks, and intensifying innovation competition. The topics of this Special Issue include, but are not limited to, those listed below:

- Multidimensional data collaborative service and data resource optimization strategy;

- Value added mathematical analysis and simulation models throughout the life cycle;

- Artificial intelligence in industrial applications;

- Sensor-based data analysis method and industrial application;

- Big data analytics and machine learning in CPS, IoT, and digital twins;

- Decision support for production planning and scheduling;

- Real-time data-driven decision-making models;

- Factory modeling, analysis, and performance evaluation;

- Mass personalization and customization design by data analytics;

- AI-enabled changes to business models and value creation processes;

- Simulation and applications for value chain digital ecosystem of dual carbon.

Prof. Dr. Kuo-Yi Lin
Prof. Dr. Kuang-Cheng Chai
Dr. Ke-Chiun Chang
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

  • machine learning
  • decision analysis
  • environmental management
  • technology strategic management

Published Papers (3 papers)

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Research

16 pages, 1011 KiB  
Article
A Cloud- and Game Model-Based Approach to Project Evaluations of Sustainable Power Supply Investments
by Kuoyi Lin and Bin Li
Sustainability 2024, 16(10), 4040; https://doi.org/10.3390/su16104040 - 11 May 2024
Viewed by 380
Abstract
In light of electrical energy’s increasing role in economic systems worldwide, prioritizing investments in sustainable power supplies has become paramount. This study proposes a model based on cloud theory and game theory to evaluate sustainable power supply investment projects. It establishes a foundation [...] Read more.
In light of electrical energy’s increasing role in economic systems worldwide, prioritizing investments in sustainable power supplies has become paramount. This study proposes a model based on cloud theory and game theory to evaluate sustainable power supply investment projects. It establishes a foundation for assessing the merits of power supply investments, which are crucial for continuous electricity provision and economic advancement. By integrating an enhanced analytic hierarchy process and the entropy method, the study develops a dual-weighted evaluative index system. This hybrid approach addresses ambiguities and enhances the weight determination accuracy, which, when applied to the Liaojiawan Transformer Substation, verifies the project’s high benefit level, corroborated by empirical data. This innovative methodology offers a strategic framework for future power supply investments. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Techniques: Towards a Sustainable Industry)
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28 pages, 3064 KiB  
Article
Research on the Key Influencing Goals for Visual Design Sustainability: A Dual Perspective
by Chia-Liang Lin, Ching-Yun Hsu and Chu-Ho Ting
Sustainability 2024, 16(5), 1885; https://doi.org/10.3390/su16051885 - 25 Feb 2024
Viewed by 678
Abstract
The United Nations established 17 sustainable development goals (SDGs) in 2015, but research on these goals in the visual design industry remains limited. This study introduces a hybrid approach, combining fuzzy analytical hierarchy process (FAHP) and grey rational analysis (GRA) to assess sustainable [...] Read more.
The United Nations established 17 sustainable development goals (SDGs) in 2015, but research on these goals in the visual design industry remains limited. This study introduces a hybrid approach, combining fuzzy analytical hierarchy process (FAHP) and grey rational analysis (GRA) to assess sustainable factors from the perspectives of both service providers and consumers. In the FAHP model, consumers and visual design professionals had similar views on the ranking of dimensions and indicators. Both reported that the most important dimension for visual design sustainability is the environment. However, the perspective of consumers differed from that of visual design practitioners in the GRA model, as consumers argued that the social aspect has the greatest impact on visual design sustainability, while practitioners believed that the environmental aspect is the most important. The main contribution of the study is to emphasise that the hybrid multi-criteria decision-making (MCDM) mode can help the visual design industry align its services to consumer expectations. A systematic and objective model that presents practical insights relevant to industry is offered by this model. It also serves as a valuable reference for future research in similar areas. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Techniques: Towards a Sustainable Industry)
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13 pages, 2639 KiB  
Article
CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images
by Irfan Javid, Rozaida Ghazali, Waddah Saeed, Tuba Batool and Ebrahim Al-Wajih
Sustainability 2024, 16(1), 117; https://doi.org/10.3390/su16010117 - 22 Dec 2023
Cited by 1 | Viewed by 933
Abstract
The escalation in vehicular traffic, in conjunction with global population growth, has precipitated heightened road congestion, augmented air pollution, and a rise in vehicular accidents. Over the past decade, the global vehicular count has witnessed a substantial surge. In this context, traffic monitoring [...] Read more.
The escalation in vehicular traffic, in conjunction with global population growth, has precipitated heightened road congestion, augmented air pollution, and a rise in vehicular accidents. Over the past decade, the global vehicular count has witnessed a substantial surge. In this context, traffic monitoring emerges as a paramount challenge, especially within developing nations. This research introduces an innovative system for vehicle detection and categorization aimed at intelligent traffic monitoring. The system utilizes a convolutional neural network-based U-Net model for the segmentation of aerial images. After segmentation, the outputs are further analyzed for vehicle identification. This vehicle detection utilizes an advanced spatial pyramid pooling (ASPP) mechanism which refines the spatial partitions of the image and captures intricate details, enhancing the accuracy and precision of the detection process. Detected vehicles are then categorized into distinct subcategories. For the effective management and control of high-density traffic flow, the extended Kalman filter (EKF) technique is employed, thereby reducing the reliance on human oversight. In experimental evaluations, our proposed model exhibits exemplary vehicle detection capabilities across the German Aerospace Center (DLR3K) and the Vehicle Detection in Aerial Imagery (VEDAI) datasets. Potential applications of the system encompass vehicle identification in traffic streams, traffic congestion assessment, intersection traffic density analysis, differentiation of vehicle types, and pedestrian pathway determination. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Techniques: Towards a Sustainable Industry)
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