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Artificial Intelligence-Based Signal Processing for Sustainability
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Artificial Intelligence-Based Signal Processing for 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 September 2023) | Viewed by 13145

Special Issue Editor

Dr. Gwanggil Jeon

E-Mail Website
Guest Editor
Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Interests: image processing; particularly image compression; motion estimation; demosaicking and image enhancement; computational intelligence, such as fuzzy and rough sets theories
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Signal processing is a significant step for the eloquent restoration and analysis of ill-posed signals which are extremely troubled in nature. Traditional signal processing approaches yield rational answers; however, scheming a refined approach for ideal presentation frequently needs several hits and trials. The deep learning technique allows for a prompt and reasonable answer by straightly mimicking the wanted output. Today, artificial intelligence is playing an increasingly important role in both image and video signals.

Artificial intelligence, deep learning, and various areas of associated technologies have contributed seriously to research into sustainability. In particular, deep learning is a significant theme that has attracted a huge amount of attention across both industry and academia. Improved from the conventional machine-learning methods, deep learning methods allow the end-to-end optimization of the whole data-driven pipeline. Deep learning also permits the learning of deep features within the dataset in numerous forms. However, until now, the most effective approaches of deep learning have been within the area of computer science and its associated engineering areas. The application of deep learning for answering environmental issues for sustainability is still incomplete in relation to the demand.

The Guest Editor seeks publications that include, but are not limited to, the following domains, related to the diverse aspects of machine learning and artificial intelligence for sustainability research.

Dr. Gwanggil Jeon
Guest Editor

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 and deep learning-based signal processing methods
  • artificial intelligence and deep learning for environment and health
  • artificial intelligence and deep learning for agriculture and industry 4.0
  • artificial intelligence and deep learning for air, water and climate sustainability
  • artificial intelligence and deep learning for smart energy, renewable energy and green fuel
  • artificial intelligence and deep learning for smart cities

Published Papers (3 papers)

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Research

20 pages, 5910 KiB  
Article
Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health
by Zhencheng Fan, Zheng Yan and Shiping Wen
Sustainability 2023, 15(18), 13493; https://0-doi-org.brum.beds.ac.uk/10.3390/su151813493 - 08 Sep 2023
Cited by 8 | Viewed by 6809
Abstract
Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building [...] Read more.
Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Signal Processing for Sustainability)
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22 pages, 576 KiB  
Article
An End-to-End Transfer Learning Framework of Source Recording Device Identification for Audio Sustainable Security
by Zhifeng Wang, Jian Zhan, Guozhong Zhang, Daliang Ouyang and Huaiyong Guo
Sustainability 2023, 15(14), 11272; https://0-doi-org.brum.beds.ac.uk/10.3390/su151411272 - 19 Jul 2023
Cited by 3 | Viewed by 785
Abstract
Source recording device identification poses a significant challenge in the field of Audio Sustainable Security (ASS). Most existing studies on end-to-end identification of digital audio sources follow a two-step process: extracting device-specific features and utilizing them in machine learning or deep learning models [...] Read more.
Source recording device identification poses a significant challenge in the field of Audio Sustainable Security (ASS). Most existing studies on end-to-end identification of digital audio sources follow a two-step process: extracting device-specific features and utilizing them in machine learning or deep learning models for decision-making. However, these approaches often rely on empirically set hyperparameters, limiting their generalization capabilities. To address this limitation, this paper leverages the self-learning ability of deep neural networks and the temporal characteristics of audio data. We propose a novel approach that utilizes the Sinc function for audio preprocessing and combine it with a Deep Neural Network (DNN) to establish a comprehensive end-to-end identification model for digital audio sources. By allowing the parameters of the preprocessing and feature extraction processes to be learned through gradient optimization, we enhance the model’s generalization. To overcome practical challenges such as limited timeliness, small sample sizes, and incremental expression, this paper explores the effectiveness of an end-to-end transfer learning model. Experimental verification demonstrates that the proposed end-to-end transfer learning model achieves both timely and accurate results, even with small sample sizes. Moreover, it avoids the need for retraining the model with a large number of samples due to incremental expression. Our experiments showcase the superiority of our method, achieving an impressive 97.7% accuracy when identifying 141 devices. This outperforms four state-of-the-art methods, demonstrating an absolute accuracy improvement of 4.1%. This research contributes to the field of ASS and provides valuable insights for future studies in audio source identification and related applications of information security, digital forensics, and copyright protection. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Signal Processing for Sustainability)
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16 pages, 3835 KiB  
Article
AI and Digital Transformation in Higher Education: Vision and Approach of a Specific University in Vietnam
by Vu Khanh Quy, Bui Trung Thanh, Abdellah Chehri, Dao Manh Linh and Do Anh Tuan
Sustainability 2023, 15(14), 11093; https://0-doi-org.brum.beds.ac.uk/10.3390/su151411093 - 16 Jul 2023
Cited by 6 | Viewed by 4741
Abstract
The Fourth Industrial Revolution is opening up new opportunities and challenges for all industries, professions, and fields, aiming to bring humanity more optimal tools and services. During the Fourth Industrial Revolution, digital transformation has been one of the most critical problems. Artificial Intelligence [...] Read more.
The Fourth Industrial Revolution is opening up new opportunities and challenges for all industries, professions, and fields, aiming to bring humanity more optimal tools and services. During the Fourth Industrial Revolution, digital transformation has been one of the most critical problems. Artificial Intelligence (AI) and the Internet of Things (IoT) are two technologies that have the potential to cause the biggest breakout to evolve in the educational domain. In recent years, digital transformation has seen implementation across all sectors, including education, healthcare, agriculture, transportation, and other smart ecosystems. Among those areas, education, especially higher education, is among the most challenging due to the diversity in training programs, duration, and subjects. The Internet of Things makes it possible to create smart and ubiquitous learning environments, while artificial intelligence can completely transform the way we learn and teach. In this paper, we present the digital transformation process in higher education in Vietnam and internationally and analyze some characteristics of Vietnamese higher education in the digital transformation process. Moreover, we present the vision, approach, and challenges to digital transformation at universities of low- and middle-income countries from the perspective of the Hung Yen University of Technology and Education in Vietnam. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Signal Processing for Sustainability)
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