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AI for Sustainable Real-World Applications
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AI for Sustainable Real-World Applications

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 11617

Special Issue Editors

Prof. Dr. Dhiya Al-Jumeily OBE

E-Mail Website
Guest Editor
Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: AI-based clinical decision making; medical knowledge engineering; patient safety; human–machine interaction; wearable and intelligent devices and instruments; AI for addressing united nations sustainable development goals; eSystem engineering; air and water pollution
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jamila Mustafina

E-Mail Website
Guest Editor
iTech Research Lab, Kazan Federal University, Kazan, Russia
Interests: AI in socially significant domains; ethics of AI
Dr. Manoj Jayabalan

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK
Interests: AI applications; ML applications

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI), Intelligent Sensors, Robotics and more recently Industry 4.0 are research areas and applications aligned to benefit the research community and society in various domains. Sensors emit a tremendous amount of data (aka big data) which can be captured and analysed using different AI and Machine Learning (ML) tools and techniques. Extensive research has been developed in this area, ranging from theoretical foundations and principles, to practical applications in diverse context including medical, industry, environment, finance, education, to name just a few.

The aim of this Special Issue is allowing researchers to communicate their high-quality and original ideas by presenting and publishing innovative advances in Industry 4.0, computational intelligence and the Internet of Everything (IoE) and their applications.

This special issue explores the convergence of Industry 4.0, AI, data science, and their applications in real-world application, providing a background to problem domains, considering the progress so far, assessing the potential of such approaches, and exploring possible future directions. We aim to increase the understanding and use of AI techniques in tackling the real-world problems. We welcome contributions that deal with all aspects of the scientific foundations, theories, techniques and applications of computing, data and analytics, including, but not limited to:

  • Industry 4.0 applications in health and medicine and other social domains
  • Advances in Image and Signal Processing
  • Computational Intelligence Technology for sustainable real-world applications (Healthcare, Medicine, Education, Business, Culture, etc.)
  • Cognitive Computing and Emotional Intelligence in sustainable real-world applications
  • Computational Intelligence Technology in Data mining, Data Integration and Big Data Analysis for sustainable real-world applications
  • Predictive Models and Analytics Using Artificial Intelligence

We look forward to receiving your contributions.

Prof. Dr. Dhiya Al-Jumeily
Prof. Dr. Jamila Mustafina
Dr. Manoj Jayabalan
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

  • AI applications in social domains
  • machine learning
  • big data
  • Internet of Things (IoT)
  • Industry 4.0

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

21 pages, 857 KiB  
Article
Assessment of Water Hydrochemical Parameters Using Machine Learning Tools
by Ivan Malashin, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov and Vadim Tynchenko
Sustainability 2025, 17(2), 497; https://doi.org/10.3390/su17020497 - 10 Jan 2025
Viewed by 1087
Abstract
Access to clean water is a fundamental human need, yet millions of people worldwide still lack access to safe drinking water. Traditional water quality assessments, though reliable, are typically time-consuming and resource-intensive. This study investigates the application of machine learning (ML) techniques for [...] Read more.
Access to clean water is a fundamental human need, yet millions of people worldwide still lack access to safe drinking water. Traditional water quality assessments, though reliable, are typically time-consuming and resource-intensive. This study investigates the application of machine learning (ML) techniques for analyzing river water quality in the Barnaul area, located on the Ob River in the Altai Krai. The research particularly highlights the use of the Water Quality Index (WQI) as a key factor in feature engineering. WQI, calculated using the Horton model, integrates nine hydrochemical parameters: pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The primary objective was to demonstrate the contribution of WQI in enhancing predictive performance for water quality analysis. A dataset of 2465 records was analyzed, with missing values for parameters (pH, sulfate, and trihalomethanes) addressed using predictive imputation via neural network (NN) architectures optimized with genetic algorithms (GAs). Models trained without WQI achieved moderate predictive accuracy, but incorporating WQI as a feature dramatically improved performance across all tasks. For the trihalomethanes model, the R2 score increased from 0.68 (without WQI) to 0.86 (with WQI). Similarly, for pH, the R2 improved from 0.35 to 0.74, and for sulfate, from 0.27 to 0.69 after including WQI in the feature set. Full article
(This article belongs to the Special Issue AI for Sustainable Real-World Applications)
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29 pages, 1064 KiB  
Article
Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools
by Ivan Malashin, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub, Aleksei Borodulin and Yadviga Tynchenko
Sustainability 2024, 16(21), 9437; https://doi.org/10.3390/su16219437 - 30 Oct 2024
Cited by 6 | Viewed by 4744
Abstract
Optimizing agricultural productivity and promoting sustainability necessitates accurate predictions of crop yields to ensure food security. Various agricultural and climatic variables are included in the analysis, encompassing crop type, year, season, and the specific climatic conditions of the Indian state during the crop’s [...] Read more.
Optimizing agricultural productivity and promoting sustainability necessitates accurate predictions of crop yields to ensure food security. Various agricultural and climatic variables are included in the analysis, encompassing crop type, year, season, and the specific climatic conditions of the Indian state during the crop’s growing season. Features such as crop and season were one-hot encoded. The primary objective was to predict yield using a deep neural network (DNN), with hyperparameters optimized through genetic algorithms (GAs) to maximize the R2 score. The best-performing model, achieved by fine-tuning its hyperparameters, achieved an R2 of 0.92, meaning it explains 92% of the variation in crop yields, indicating high predictive accuracy. The optimized DNN models were further analyzed using explainable AI (XAI) techniques, specifically local interpretable model-agnostic explanations (LIME), to elucidate feature importance and enhance model interpretability. The analysis underscored the significant role of features such as crops, leading to the incorporation of an additional dataset to classify the most optimal crops based on more detailed soil and climate data. This classification task was also executed using a GA-optimized DNN, aiming to maximize accuracy. The results demonstrate the effectiveness of this approach in predicting crop yields and classifying optimal crops. Full article
(This article belongs to the Special Issue AI for Sustainable Real-World Applications)
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19 pages, 4124 KiB  
Article
An Enhanced Particle Swarm Optimization Long Short-Term Memory Network Hybrid Model for Predicting Residential Daily CO2 Emissions
by Yuyi Hu, Bojun Wang, Yanping Yang and Liwei Yang
Sustainability 2024, 16(20), 8790; https://doi.org/10.3390/su16208790 - 11 Oct 2024
Cited by 4 | Viewed by 1370
Abstract
This study aims to establish an accurate hybrid model for predicting residential daily carbon dioxide (CO2) emissions, offering essential theoretical insights and data support for decision-makers in the construction industry. A hybrid model named CRLPSO-LSTM was proposed, which integrates an enhanced [...] Read more.
This study aims to establish an accurate hybrid model for predicting residential daily carbon dioxide (CO2) emissions, offering essential theoretical insights and data support for decision-makers in the construction industry. A hybrid model named CRLPSO-LSTM was proposed, which integrates an enhanced particle swarm optimization (CRLPSO) algorithm with a long short-term memory (LSTM) network. The CRLPSO algorithm enhances population quality, diversity, and global search efficiency by introducing improved circle chaotic mapping, optimizing worst mutations, and incorporating the Lévy flight strategy. The performance of the CRLPSO algorithm was rigorously evaluated using 23 internationally recognized standard test functions. Subsequently, the CRLPSO algorithm was employed to optimize the parameters of the LSTM model. Experimental validation was performed on three datasets from China, the United States, and Russia, each exhibiting distinct emissions characteristics: China with high emissions and high volatility, the United States with medium emissions and medium volatility, and Russia with low emissions and low volatility. The results indicate that the CRLPSO-LSTM hybrid model outperformed other hybrid models in predicting residential daily CO2 emissions, as demonstrated by superior R2, MAE, and MSE metrics. This study underscores the effectiveness and broad applicability of the CRLPSO-LSTM hybrid model, offering a robust theoretical foundation and data support for advancing the sustainable development goals. Full article
(This article belongs to the Special Issue AI for Sustainable Real-World Applications)
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17 pages, 711 KiB  
Article
Statistics Using Neural Networks in the Context of Sustainable Development Goal 9.5
by Valery Okulich-Kazarin
Sustainability 2024, 16(19), 8395; https://doi.org/10.3390/su16198395 - 26 Sep 2024
Cited by 4 | Viewed by 1492
Abstract
In recent years neural networks have been used to achieve all 17 SDGs. This paper is directly related to SDG 9. In particular, the application of neural networks in statistics indicates the creation and development of a scientific research infrastructure (including encouraging innovation, [...] Read more.
In recent years neural networks have been used to achieve all 17 SDGs. This paper is directly related to SDG 9. In particular, the application of neural networks in statistics indicates the creation and development of a scientific research infrastructure (including encouraging innovation, SDG 9.5). Also, this paper shows the possibility of the mass practical application of neural networks for statistics in the context of sustainable development (with the possibilit of increasing the number of researchers, SDG 9.5). The paper aims to test the following two hypotheses in the context of SDG 9.5: (1) The rapid growth of scientific interest in neural networks will lead to a decrease in the number of scientific publications in statistics. (2) It is possible to use neural networks for calculating statistical indicators. Bibliometric analysis, mathematical modeling, the calculation of statistical indicators using the new prompt and Excel table z-statistics were used. The scientific novelty lies in the new knowledge obtained by the author for the first time. This study integrates advanced technologies (neural networks) and a traditional field (statistics), which is a significant contribution to innovation and infrastructure development (Indicator 9.5.1). The practical value lies in the ease of the mass use of neural networks for statistical data processing of more than 100,000 units, which is related to Indicator 9.5.2. Thus, this paper represents an important contribution to the stimulation of innovation, thereby building up technological potential and leading to a significant increase in the number of researchers (SDG 9.5). Full article
(This article belongs to the Special Issue AI for Sustainable Real-World Applications)
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40 pages, 7682 KiB  
Article
Digital Visualization of Environmental Risk Indicators in the Territory of the Urban Industrial Zone
by Ruslan Safarov, Zhanat Shomanova, Yuriy Nossenko, Zhandos Mussayev and Ayana Shomanova
Sustainability 2024, 16(12), 5190; https://doi.org/10.3390/su16125190 - 18 Jun 2024
Cited by 2 | Viewed by 1403
Abstract
This study focused on predicting the spatial distribution of environmental risk indicators using mathematical modeling methods including machine learning. The northern industrial zone of Pavlodar City in Kazakhstan was used as a model territory for the case. Nine models based on the methods [...] Read more.
This study focused on predicting the spatial distribution of environmental risk indicators using mathematical modeling methods including machine learning. The northern industrial zone of Pavlodar City in Kazakhstan was used as a model territory for the case. Nine models based on the methods kNN, gradient boosting, artificial neural networks, Kriging, and multilevel b-spline interpolation were employed to analyze pollution data and assess their effectiveness in predicting pollution levels. Each model tackled the problem as a regression task, aiming to estimate the pollution load index (PLI) values for specific locations. It was revealed that the maximum PLI values were mainly located to the southwest of the TPPs over some distance from their territories according to the average wind rose for Pavlodar City. Another area of high PLI was located in the northern part of the studied region, near the Hg-accumulating ponds. The high PLI level is generally attributed to the high concentration of Hg. Each studied method of interpolation can be used for spatial distribution analysis; however, a comparison with the scientific literature revealed that Kriging and MLBS interpolation can be used without extra calculations to produce non-linear, empirically consistent, and smooth maps. Full article
(This article belongs to the Special Issue AI for Sustainable Real-World Applications)
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