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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 25605

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Special Issue Editors

Dr. Muhammad Syafrudin

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Guest Editor

Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
Interests: machine learning; artificial intelligence; information system; IoT; health informatics
Special Issues, Collections and Topics in MDPI journals

Dr. Ganjar Alfian

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Guest Editor

Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia
Interests: machine learning; artificial intelligence; information system; IoT; health informatics
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Dr. Norma Latif Fitriyani

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Guest Editor

Department of Data Science, Sejong University, Seoul, Republic of Korea
Interests: data mining and analysis; machine learning; image processing; artificial intelligence; health informatics
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Dr. Muhammad Anshari

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Guest Editor

UBD School of Business and Economics, Univesiti Brunei Darussalam, Bandar Seri Begawan BE 1410, Brunei
Interests: business information systems; knowledge management systems; digital business & digital humanities; big data in business; ICT & area studies (ASEAN/Borneo); ICT in education; e-health & mobile health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern civilization is surrounded by a technologically networked environment. Many of the applications created for this digital ecosystem employ advanced artificial intelligence techniques to solve a variety of problems: ranging from improved search engines to advanced facial recognition features on the web; from shape recognition algorithms for image processing to pattern recognition methods for social networks and economic studies; and from complex behavioral engines for synthetic characters in computer-generated images in movies and video games to advanced routing algorithms. Artificial intelligence has the potential to transform the world in the future decades, from corporate to domestic uses.

It is predicted that AI will contribute more to the global economy than China and India combined. It is also expected that, within the next 10 years, practically every successful industry or corporation would employ some form of artificial intelligence to ensure that their operations run smoothly and efficiently.

This Special Issue aims to disseminate the most recent artificial intelligence research results and breakthroughs, with a particular emphasis on their practical applications in science, engineering, industry, medical, robotics, manufacturing, entertainment, optimization, business, and other sectors. Researchers and practitioners are invited to submit high-quality original research or review articles on these subjects for consideration in this Special Issue.

The topics of interest for this Special Issue include, but are not limited to, novel applications of:

Internet of Things (IoT) and Cyber-Physical Systems (CPS);
Intelligent Transportation Systems (ITS) and smart vehicles;
Analyzing big data and interpreting complex networks;
Deep learning and real-world applications;
Neural networks, fuzzy systems, and neuro-fuzzy systems;
Architectures, methods, and approaches for distributed AI systems;
Decision-support systems, including evolutionary algorithms, swarm intelligence, nature and biologically inspired meta-heuristics, and so on;
Knowledge representation, expert systems, and knowledge processing;
Image processing, pattern recognition, and speech recognition;
Detection, analysis, diagnostics, and monitoring of intelligent faults;
Practical applications with the aforementioned approaches in the industry, such as case studies or benchmarking.

You may choose our Joint Special Issue in BDCC.

Dr. Muhammad Syafrudin
Dr. Ganjar Alfian
Dr. Norma Latif Fitriyani
Dr. Muhammad Anshari
Guest Editors

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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

Internet of Things (IoT)
Cyber-Physical Systems (CPS)
Intelligent Transportation Systems (ITS) and smart vehicles
analyzing big data and interpreting complex networks
deep learning and real-world applications
neural networks, fuzzy systems, and neuro-fuzzy systems
architectures, methods, and approaches for distributed ai systems
decision-support systems, including evolutionary algorithms, swarm intelligence, nature and biologically inspired meta-heuristics
knowledge representation, expert systems, and knowledge processing
image processing, pattern recognition, and speech recognition
detection, analysis, diagnostics, and monitoring of intelligent faults

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Published Papers (9 papers)

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Editorial

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5 pages, 163 KiB

Open AccessEditorial

Applied Artificial Intelligence for Sustainability

by Muhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani and Muhammad Anshari

Sustainability 2024, 16(6), 2469; https://doi.org/10.3390/su16062469 - 15 Mar 2024

Cited by 1 | Viewed by 1174

Abstract

In the contemporary era, modern civilization is immersed in a technologically interconnected environment, where numerous applications within the digital ecosystem harness advanced artificial intelligence (AI) techniques [...] Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

Research

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24 pages, 4298 KiB

Open AccessArticle

Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages

by Zahra Ahanin, Maizatul Akmar Ismail, Narinderjit Singh Sawaran Singh and Ammar AL-Ashmori

Sustainability 2023, 15(16), 12539; https://doi.org/10.3390/su151612539 - 18 Aug 2023

Cited by 14 | Viewed by 2028

Abstract

Emotions are vital for identifying an individual’s attitude and mental condition. Detecting and classifying emotions in Natural Language Processing applications can improve Human–Computer Interaction systems, leading to effective decision making in organizations. Several studies on emotion classification have employed word embedding as a feature extraction method, but they do not consider the sentiment polarity of words. Moreover, relying exclusively on deep learning models to extract linguistic features may result in misclassifications due to the small training dataset. In this paper, we present a hybrid feature extraction model using human-engineered features combined with deep learning based features for emotion classification in English text. The proposed model uses data augmentation, captures contextual information, integrates knowledge from lexical resources, and employs deep learning models, including Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representation and Transformer (BERT), to address the issues mentioned above. The proposed model with hybrid features attained the highest Jaccard accuracy on two of the benchmark datasets, with 68.40% on SemEval-2018 and 53.45% on the GoEmotions dataset. The results show the significance of the proposed technique, and we can conclude that the incorporation of the hybrid features improves the performance of the baseline models. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

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17 pages, 3348 KiB

Open AccessArticle

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

by Le Gao, Kun Wang, Xin Zhang and Chen Wang

Sustainability 2023, 15(13), 10269; https://doi.org/10.3390/su151310269 - 28 Jun 2023

Cited by 2 | Viewed by 2298

Abstract

In recent years, the intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the main research frontiers in the field of geological and metallogenic prediction. However, this method also has many problems that need to be solved. For example: (1) There are very few trainable image samples containing mineral point labels; (2) the geological image features are small and irregular, and the image similarity is high; (3) it is difficult to calculate the influence of different geological prospecting factors on ore mineralization. Based on this, this paper constructs a deep learning network model multiscale feature attention framework (MFAF) based on geoimage data. The results show that the MFCA-Net module in the MFAF model can solve the problem of scarce mine label images to a certain extent. In addition, the channel attention mechanism SE-Net module can quantify the difference in influence of different source factors on mineralization. The prediction map is obtained by applying the MFAF model in the study of deposit identification and prediction in the research area of the southern section of the Qin-hang metallogenic belt. The experimental results show that the areas numbered 5, 9, 16, 28, 34, 41, 50, 72, 74, 75, 80, 97, 101, 124, and 130 have great metallogenic potential and this method would be a promising tool for metallogenic prediction. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved. The multi-scale feature fusion and attention mechanism MFAF in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantees and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

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18 pages, 3919 KiB

Open AccessArticle

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

by Khasim Vali Dudekula, Hussain Syed, Mohamed Iqbal Mahaboob Basha, Sudhakar Ilango Swamykan, Purna Prakash Kasaraneni, Yellapragada Venkata Pavan Kumar, Aymen Flah and Ahmad Taher Azar

Sustainability 2023, 15(3), 2206; https://doi.org/10.3390/su15032206 - 25 Jan 2023

Cited by 29 | Viewed by 3824

Abstract

The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

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32 pages, 13215 KiB

Open AccessArticle

Performance Analysis of Classification and Detection for PV Panel Motion Blur Images Based on Deblurring and Deep Learning Techniques

by Abdullah Ahmed Al-Dulaimi, Muhammet Tahir Guneser, Alaa Ali Hameed, Fausto Pedro García Márquez, Norma Latif Fitriyani and Muhammad Syafrudin

Sustainability 2023, 15(2), 1150; https://doi.org/10.3390/su15021150 - 7 Jan 2023

Cited by 3 | Viewed by 2058

Abstract

Detecting snow-covered solar panels is crucial as it allows us to remove snow using heating techniques more efficiently and restores the photovoltaic system to proper operation. This paper presents classification and detection performance analyses for snow-covered solar panel images. The classification analysis consists of two cases, and the detection analysis consists of one case based on three backbones. In this study, five deep learning models, namely visual geometry group-16 (VGG-16), VGG-19, residual neural network-18 (RESNET-18), RESNET-50, and RESNET-101, are used to classify solar panel images. The models are trained, validated, and tested under different conditions. The first case of classification is performed on the original dataset without preprocessing. In the second case, extreme climate conditions are simulated by generating motion noise; furthermore, the dataset is replicated using the upsampling technique to handle the unbalancing issue. For the detection case, a region-based convolutional neural network (RCNN) detector is used to detect the three categories of solar panels, which are all_snow, no_snow, and partial. The dataset of these categories is taken from the second case in the classification approach. Finally, we proposed a blind image deblurring algorithm (BIDA) that can be a preprocessing step before the CNN (BIDA-CNN) model. The accuracy of the models was compared and verified; the accuracy results show that the proposed CNN-based blind image deblurring algorithm (BIDA-CNN) outperformed other models evaluated in this study. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

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15 pages, 4789 KiB

Open AccessArticle

Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis

by Wangpeng He, Peipei Zhang, Xuan Liu, Binqiang Chen and Baolong Guo

Sustainability 2022, 14(24), 16793; https://doi.org/10.3390/su142416793 - 14 Dec 2022

Cited by 1 | Viewed by 1500

Abstract

Extracting weak fault features from noisy measured signals is critical for the diagnosis of wind turbine faults. In this paper, a novel group-sparse feature extraction method via an ensemble generalized minimax-concave (GMC) penalty is proposed for machinery health monitoring. Specifically, the proposed method tackles the problem of formulating large useful magnitude values as isolated features in the original GMC-based sparse feature extraction method. To accurately estimate group-sparse fault features, the proposed method formulates an effective unconstrained optimization problem wherein the group-sparse structure is incorporated into non-convex regularization. Moreover, the convex condition is proved to maintain the convexity of the whole formulated cost function. In addition, the setting criteria of the regularization parameter are investigated. A simulated signal is presented to verify the performance of the proposed method for group-sparse feature extraction. Finally, the effectiveness of the proposed group-sparse feature extraction method is further validated by experimental fault diagnosis cases. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

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19 pages, 2735 KiB

Open AccessArticle

Teacher-Assistant Knowledge Distillation Based Indoor Positioning System

by Aqilah Binti Mazlan, Yin Hoe Ng and Chee Keong Tan

Sustainability 2022, 14(21), 14652; https://doi.org/10.3390/su142114652 - 7 Nov 2022

Cited by 5 | Viewed by 1729

Abstract

Indoor positioning systems have been of great importance, especially for applications that require the precise location of objects and users. Convolutional neural network-based indoor positioning systems (IPS) have garnered much interest in recent years due to their ability to achieve high positioning accuracy and low positioning error, regardless of signal fluctuation. Nevertheless, a powerful CNN framework comes with a high computational cost. Hence, there will be difficulty in deploying such a system on a computationally restricted device. Knowledge distillation has been an excellent solution which allows smaller networks to imitate the performance of larger networks. However, problems such as degradation in the student’s positioning performance, occur when a far more complex CNN is used to train a small CNN, because the small CNN does not have the ability to fully capture the knowledge that has been passed down. In this paper, we implemented the teacher-assistant framework to allow a simple CNN indoor positioning system to closely imitate a superior indoor positioning scheme. The framework involves transferring knowledge from a large pre-trained network to a small network by passing through an intermediate network. Based on our observation, the positioning error of a small network can be reduced to up to 38.79% by implementing the teacher-assistant knowledge distillation framework, while a typical knowledge distillation framework can only reduce the error to 30.18%. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

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19 pages, 4161 KiB

Open AccessArticle

Deep Learning Approach for the Detection of Noise Type in Ancient Images

by Poonam Pawar, Bharati Ainapure, Mamoon Rashid, Nazir Ahmad, Aziz Alotaibi and Sultan S. Alshamrani

Sustainability 2022, 14(18), 11786; https://doi.org/10.3390/su141811786 - 19 Sep 2022

Cited by 12 | Viewed by 3213

Abstract

Recent innovations in digital image capturing techniques facilitate the capture of stationary and moving objects. The images can be easily captured via high-end digital cameras, mobile phones and other handheld devices. Most of the time, captured images vary compared to actual objects. The captured images may be contaminated by dark, grey shades and undesirable black spots. There are various reasons for contamination, such as atmospheric conditions, limitations of capturing device and human errors. There are various mechanisms to process the image, which can clean up contaminated image to match with the original one. The image processing applications primarily require detection of accurate noise type which is used as input for image restoration. There are filtering techniques, fractional differential gradient and machine learning techniques to detect and identify the type of noise. These methods primarily rely on image content and spatial domain information of a given image. With the advancements in the technologies, deep learning (DL) is a technology that can be trained to mimic human intelligence to recognize various image patterns, audio files and text for accuracy. A deep learning framework empowers correct processing of multiple images for object identification and quick decision abilities without human interventions. Here Convolution Neural Network (CNN) model has been implemented to detect and identify types of noise in the given image. Over the multiple internal iterations to optimize the results, the identified noise is classified with 99.25% accuracy using the Proposed System Architecture (PSA) compared with AlexNet, Yolo V5, Yolo V3, RCNN and CNN. The proposed model in this study proved to be suitable for the classification of mural images on the basis of every performance parameter. The precision, accuracy, f1-score and recall of the PSA are 98.50%, 99.25%, 98.50% and 98.50%, respectively. This study contributes to the development of mural art recovery. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

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Other

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20 pages, 2191 KiB

Open AccessSystematic Review

A Systematic Literature Review of Vehicle Routing Problems with Time Windows

by Xiaobo Liu, Yen-Lin Chen, Lip Yee Por and Chin Soon Ku

Sustainability 2023, 15(15), 12004; https://doi.org/10.3390/su151512004 - 4 Aug 2023

Cited by 9 | Viewed by 6001

Abstract

Vehicle routing problems with time windows (VRPTW) have gained a lot of attention due to their important role in real-life logistics and transport. As a result of the complexity of real-life situations, most problems are multi-constrained and multi-objective, which increases their difficulty. The aim of this paper is to contribute to the effective solution of VRPTW-related problems. Therefore, research questions and objectives are set in accordance with PRISMA guidelines, and data extraction and analysis of the relevant literature within the last five years (2018–2022) are compared to answer the set research questions. The results show that approximately 86% of the algorithms involved in the literature are approximate methods, with more meta-heuristics than heuristics, and nearly 40% of the literature uses hybrid methods combining two or more algorithms. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence for Sustainability)

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