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

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 60632

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Special Issue Editors

Prof. Dr. Ah-Lian Kor

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Guest Editor

School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS6 3QS, UK
Interests: machine learning; intelligent agents; cloud and edge intelligence; IoT; smart buildings; smart manufacturing; qualitative reasoning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Eric Rondeau

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Guest Editor

Faculté des Sciences et Technologies, University of Lorraine, Vandoeuvre Les Nancy, France
Interests: networks; green IT/neworking; industrial networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Karl Andersson

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Guest Editor

Pervasive and Mobile Computing Luleå, University of Technology, SE-93187 Skellefteå, Sweden
Interests: pervasive and mobile computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

“AI for Sustainability and Innovation” aims to support GESI’s Smarter2030 initiative and the UN’s Sustainable Development Goals in order to contribute to a sustainable future. This theme encompasses theoretical and applied research to address challenges relating to society and human needs (SDG2 Zero Hunger: autonomous machines, smart, and precision agriculture to increase productivity and reduce waste; SDG3 Good Health and Well Being: smart and personalized health; SDG4 Quality Education: smart and personalized educational technologies), sustainable amenities and utilities for the environment (SDG7 Affordable and Clean Energy: smart grid, smart microgrid, and smart renewable energy management system; SDG13 Climate Change via Low Carbon Growth: smart technologies to reduce energy, as well as resource consumption and waste emissions; SDG11 Sustainable Cities and Communities: smart sustainable cities and infrastructure), and sustainable industry (SDG9 Industry, Innovation, and Infrastructure: smart technologies to support Industry 4.0; SDG12 Responsible Consumption and Production: smart technologies for resource optimization, energy efficiency, and waste reduction). In summary, this Special Issue, titled “AI for Sustainability and Innovation”, calls for AI-enabled research (position, theoretical, or applied) that address relevant SDGs across the following sectors (listed in GESI’s Smarter 2020 initiative): business, power, transportation, manufacturing, services (education and health), agriculture, and buildings.

We are honored to be given the opportunity to undertake this Special Issue titled “AI for Sustainability and Innovation”. This research theme is very current and cutting edge, considering the pervasiveness of AI in every sector (see the summary for details). We would like to cordially invite you to submit any of the following: survey papers that encompass a relevant comprehensive and critical literature review; theoretical research papers that address underlying design concepts, theories, principles, or algorithms; or applied research papers that address the implementation, deployment, and evaluation of relevant smart technologies.

Thank you very much and we look forward to receiving submission of your quality research work.

Kind Regards,

Prof. Dr. Ah-Lian Kor
Prof. Dr. Eric Rondeau
Prof. Dr. Karl Andersson
Guest Editors

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Keywords

artificial intelligence
sustainable IT
innovation
smart and pervasive technologies
smart systems
cloud computing
green networking
mobile technologies
machine learning
Internet of Things

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Published Papers (11 papers)

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Research

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23 pages, 6851 KiB

Open AccessArticle

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)

by Syed Ijaz Ul Haq, Muhammad Naveed Tahir and Yubin Lan

Appl. Sci. 2023, 13(15), 8840; https://doi.org/10.3390/app13158840 - 31 Jul 2023

Cited by 9 | Viewed by 4017

Abstract

In the present study, we used device visualization in tandem with deep learning to detect weeds in the wheat crop system in actual time. We selected the PMAS Arid Agriculture University research farm and wheat crop fields in diverse weather environments to collect the weed images. Some 6000 images were collected for the study. Throughout the season, tfhe databank was assembled to detect the weeds. For this study, we used two different frameworks, TensorFlow and PyTorch, to apply deep learning algorithms. PyTorch’s implementation of deep learning algorithms performed comparatively better than that of TensorFlow. We concluded that the neural network implemented through the PyTorch framework achieves a superior outcome in speed and accuracy compared to other networks, such as YOLO variants. This work implemented deep learning models for weed detection using different frameworks. While working on real-time detection models, it is very important to consider the inference time and detection accuracy. Therefore, we have compared the results in terms of execution time and prediction accuracy. In particular, the accuracy of weed removal from wheat crops was judged to be 0.89 and 0.91, respectively, with inference times of 9.43 ms and 12.38 ms on the NVIDIA RTX2070 GPU for each picture (640 × 640). Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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17 pages, 6360 KiB

Open AccessArticle

Collaborative Indoor Positioning by Localization Comparison at an Encounter Position

by Kohei Kageyama, Tomo Miyazaki, Yoshihiro Sugaya and Shinichiro Omachi

Appl. Sci. 2023, 13(12), 6962; https://doi.org/10.3390/app13126962 - 9 Jun 2023

Cited by 2 | Viewed by 1465

Abstract

With the widespread use of smartphones, there is a surging demand for localization in indoor environments. The main challenges are the requirement of special equipment (e.g., a map database and Wi-Fi access points) and error accumulation for indoor localization. In this paper, we propose a novel collaborative indoor positioning method to reduce error accumulation. Estimated positions are corrected using the collaborator’s positions when an encounter is detected by communication based on Bluetooth Low Energy (BLE). In addition, a map is obtained by taking photos of information boards. Therefore, the proposed method needs smartphones only; other equipment is not required. We obtained an accurate localization comparison using a machine learning model. The experimental results showed that the proposed method achieved reliable encounter communication in eight facilities. The collaborative localization method successfully enhanced position estimations. Specifically, the proposed method outperformed the existing baseline method by 13.0% in accuracy of indoor positioning. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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24 pages, 840 KiB

Open AccessArticle

The Innovative Use of Intelligent Chatbot for Sustainable Health Education Admission Process: Learnt Lessons and Good Practices

by Sorin Claudiu Man, Oliviu Matei, Tudor Faragau, Laura Andreica and Dinu Daraba

Appl. Sci. 2023, 13(4), 2415; https://doi.org/10.3390/app13042415 - 13 Feb 2023

Cited by 6 | Viewed by 2973

Abstract

This article presents the methodology of creation of an innovative used by intelligent chatbots which support the admission process in universities. The lifecycle of the ontology, unlike the classical lifecycles, has six stages: conceptualization, formalization, development, testing, production and maintenance. This leads to sustainability of the chatbot, called Ana, which has been implemented at the “Iuliu Hatieganu” University of Medicine and Pharmacy from Cluj-Napoca during the admission session throughout July–September 2022, for international candidates. The sustainability of the chatbot comes from the continuous maintenance and updates of the ontology, based on candidates’ interraction with the system and updates of the admission procedures. Over time, the chatbot is able to answer the questions according to the present situation of the admission and the real needs of the candidates. Ana had a huge impact, succeeding to resolve a number of 5173 applicants requests, and only 809 messages was transferred to the human operators, statistics which show a high cost-benefit improvement in terms of reducing the travel expenses for the candidates and also for the university. The article also summarizes the good practices in developing and use of such an intelligent chatbot. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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14 pages, 2764 KiB

Open AccessArticle

Integrated Carbon Emissions and Carbon Costs for Bridge Construction Projects Using Carbon Trading and Tax Systems—Taking Beijing as an Example

by Jingjing Wang, Ke Pan, Cong Wang, Wenxiang Liu, Jiajia Wei, Kun Guo and Zhansheng Liu

Appl. Sci. 2022, 12(20), 10589; https://doi.org/10.3390/app122010589 - 20 Oct 2022

Cited by 3 | Viewed by 3108

Abstract

Bridges are special infrastructures that emit large amounts of carbon dioxide from construction. Attention should be given to the carbon cost generated by the bridge, which includes its direct economic cost; the carbon cost is the largest driving force encouraging the enterprise to implement carbon emission reduction measures. In this study, the life cycle assessment (LCA) method is applied to carbon emissions in the bridge construction stage, which include emissions from material production, transportation and on-site construction; then, a carbon emission calculation model for the construction stage is established. Next, the carbon cost calculation model for the bridge in the construction stage is determined by combining the carbon pricing mechanisms of carbon emission taxing and trading to monetize carbon emissions. Finally, by taking a bridge in Beijing as an example, the carbon emissions in the bridge construction stage are calculated, and the carbon cost is calculated. The results show that carbon emission monetization is beneficial for clarifying the environmental impact of bridge construction; these calculations should be included in cost accounting. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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21 pages, 48331 KiB

Open AccessArticle

OkeyDoggy3D: A Mobile Application for Recognizing Stress-Related Behaviors in Companion Dogs Based on Three-Dimensional Pose Estimation through Deep Learning

by Rim Yu and Yongsoon Choi

Appl. Sci. 2022, 12(16), 8057; https://doi.org/10.3390/app12168057 - 11 Aug 2022

Cited by 2 | Viewed by 3289

Abstract

Dogs often express their stress through physical motions that can be recognized by their owners. We propose a mobile application that analyzes companion dog’s behavior and their three-dimensional poses via deep learning. As existing research on pose estimation has focused on humans, obtaining a large dataset comprising images showing animal joint locations is a challenge. Nevertheless, we generated such a dataset and used it to train an AI model. Furthermore, we analyzed circling behavior, which is associated with stress in companion dogs. To this end, we used the VideoPose3D model to estimate the 3D poses of companion dogs from the 2D pose estimation technique derived by the DeepLabCut model and developed a mobile app that provides analytical information on the stress-related behaviors, as well as the walking and isolation times, of companion dogs. Finally, we interviewed five certified experts to evaluate the validity and applicability of the app. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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20 pages, 2785 KiB

Open AccessArticle

Analyzing and Visualizing Text Information in Corporate Sustainability Reports Using Natural Language Processing Methods

by Hyewon Kang and Jinho Kim

Appl. Sci. 2022, 12(11), 5614; https://doi.org/10.3390/app12115614 - 1 Jun 2022

Cited by 10 | Viewed by 5934

Abstract

Sustainability is a major contemporary issue that affects everyone. Many companies now produce an annual sustainability report, mainly intended for their stakeholders and the public, enumerating their goals and degrees of achievement regarding sustainable development. Although sustainability reports are an important resource to understand a company’s sustainability strategies and practices, the difficulty of extracting key information from dozens or hundreds of pages with sustainability and business jargon has highlighted the need for metrics to effectively measure the content of such reports. Accordingly, many researchers have attempted to analyze the concepts and messages from sustainability reports using various natural language processing (NLP) methods. In this study, we propose a novel approach that overcomes the shortcomings of previous studies. Using the sentence similarity method and sentiment analysis, the study clearly shows thematic practices and trends, as well as a significant difference in the balance of positive and negative information in the reports across companies. The results of sentiment analysis prove that the new approach of this study is very useful. It confirms that companies actively use the sustainability report to improve their positive image when they experience a crisis. It confirms that companies actively use the sustainability report to improve their positive image when they experience a crisis. The inferences gained from this method will not only help companies produce better reports that can be utilized effectively, but also provide researchers with ideas for further research. In the concluding section, we summarize the implications of our approach and discuss limitations and future research areas. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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17 pages, 1408 KiB

Open AccessArticle

The Competitive Advantage of the Indian and Korean Film Industries: An Empirical Analysis Using Natural Language Processing Methods

by Hyewon Kang, Wenyan Yin, Jinho Kim and Hwy-Chang Moon

Appl. Sci. 2022, 12(9), 4592; https://doi.org/10.3390/app12094592 - 30 Apr 2022

Cited by 2 | Viewed by 4737

Abstract

India has a longstanding reputation in the film industry, whereas South Korean films have only recently achieved notable success globally. Despite their significant positions in the global film market, there are very few studies that compare and analyze the competitive advantage of the two countries in the film industry. This paper adopts the ABCD model as a complementary framework to the two mainstream theories of strategic management (i.e., industry-based view and resource-based view) to analyze and compare the competitiveness of the industrial success of emerging countries. For the empirical test, this paper uses natural language processing methods to operationalize the theoretical model. After collecting text data from news articles in English related to the Korean and Indian film industries, this study analyzes how many keywords with regards to the 8 sub-factors of the ABCD model are mentioned in the articles using the document similarity measurement. The results reveal the different but complementary areas of strengths. India has higher competitiveness in the factor of Agility while Korea has higher competitiveness in Convergence. This study also highlights the areas for further development and potential partnership between the two countries by leveraging each other’s strengths. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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29 pages, 2983 KiB

Open AccessArticle

Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles

by Ngo Le Huy Hien and Ah-Lian Kor

Appl. Sci. 2022, 12(2), 803; https://doi.org/10.3390/app12020803 - 13 Jan 2022

Cited by 30 | Viewed by 8489

Abstract

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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23 pages, 5708 KiB

Open AccessArticle

Application of Online Transportation Mode Recognition in Games

by Emil Hedemalm, Ah-Lian Kor, Josef Hallberg, Karl Andersson, Colin Pattinson and Marta Chinnici

Appl. Sci. 2021, 11(19), 8901; https://doi.org/10.3390/app11198901 - 24 Sep 2021

Viewed by 2672

Abstract

It is widely accepted that human activities largely contribute to global emissions and thus, greatly impact climate change. Awareness promotion and adoption of green transportation mode could make a difference in the long term. To achieve behavioural change, we investigate the use of a persuasive game utilising online transportation mode recognition to afford bonuses and penalties to users based on their daily choices of transportation mode. To facilitate an easy identification of transportation mode, classification predictive models are built based on accelerometer and gyroscope historical data. Preliminary results show that the classification true-positive rate for recognising 10 different transportation classes can reach up to 95% when using a historical set (66% without). Results also reveal that the random tree classification model is a viable choice compared to random forest in terms of sustainability. Qualitative studies of the trained classifiers and measurements of Android-device gravity also raise several issues that could be addressed in future work. This research work could be enhanced through acceleration normalisation to improve device and user ambiguity. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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15 pages, 4065 KiB

Open AccessFeature PaperArticle

AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling

by Saki Gerassis, Eduardo Giráldez, María Pazo-Rodríguez, Ángeles Saavedra and Javier Taboada

Appl. Sci. 2021, 11(17), 7914; https://doi.org/10.3390/app11177914 - 27 Aug 2021

Cited by 13 | Viewed by 8436

Abstract

Mining engineers and environmental experts around the world still identify and evaluate environmental risks associated with mining activities using field-based, basic qualitative methods The main objective is to introduce an innovative AI-based approach for the construction of environmental impact assessment (EIA) indexes that statistically reflects and takes into account the relationships between the different environmental factors, finding relevant patterns in the data and minimizing the influence of human bias. For that, an AutoML process developed with Bayesian networks is applied to the construction of an interactive EIA index tool capable of assessing dynamically the potential environmental impacts of a slate mine in Galicia (Spain) surrounded by the Natura 2000 Network. The results obtained show the moderate environmental impact of the whole exploitation; however, the strong need to protect the environmental factors related to surface and subsurface runoff, species or soil degradation was identified, for which the information theory results point to a weight between 6 and 12 times greater than not influential variables. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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Review

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35 pages, 6490 KiB

Open AccessReview

Six-Gear Roadmap towards the Smart Factory

by Amr T. Sufian, Badr M. Abdullah, Muhammad Ateeq, Roderick Wah and David Clements

Appl. Sci. 2021, 11(8), 3568; https://doi.org/10.3390/app11083568 - 15 Apr 2021

Cited by 31 | Viewed by 11211

Abstract

The fourth industrial revolution is the transformation of industrial manufacturing into smart manufacturing. The advancement of digital technologies that make the trend Industry 4.0 are considered as the transforming force that will enable this transformation. However, Industry 4.0 digital technologies need to be connected, integrated and used effectively to create value and to provide insightful information for data driven manufacturing. Smart manufacturing is a journey and requires a roadmap to guide manufacturing organizations for its adoption. The objective of this paper is to review different methodologies and strategies for smart manufacturing implementation to propose a simple and a holistic roadmap that will support the transition into smart factories and achieve resilience, flexibility and sustainability. A comprehensive review of academic and industrial literature was preformed based on multiple stage approach and chosen criteria to establish existing knowledge in the field and to evaluate latest trends and ideas of Industry 4.0 and smart manufacturing technologies, techniques and applications in the manufacturing industry. These criteria are sub-grouped to fit within various stages of the proposed roadmap and attempts to bridge the gap between academia and industry and contributes to a new knowledge in the literature. This paper presents a conceptual approach based on six stages. In each stage, key enabling technologies and strategies are introduced, the common challenges, implementation tips and case studies of industrial applications are discussed to potentially assist in a successful adoption. The significance of the proposed roadmap serve as a strategic practical tool for rapid adoption of Industry 4.0 technologies for smart manufacturing and to bridge the gap between the advanced technologies and their application in manufacturing industry, especially for SMEs. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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