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Machine Learning and AI Technology for Sustainable Development
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Machine Learning and AI Technology for Sustainable Development

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 7220

Special Issue Editors

Dr. Wei-Chen Wu

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Guest Editor
Department of Finance, National Taipei University of Business, Taipei City 10051, Taiwan
Interests: machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Jason C. Hung

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung City 404348, Taiwan
Interests: e-learning; intelligent system; social computing; affective computing; multimedia system; artificial intelligence
Dr. Yuchih Wei

E-Mail Website
Guest Editor
Department of Information and Finance Management, National Taipei University of Technology, Taipei City 10608, Taiwan
Interests: artificial intelligence
Dr. Jui-hung Kao

E-Mail
Guest Editor
Department of Information Management, Shih Hsin University, Taipei 116005, Taiwan
Interests: spatial information integrated application technology; medical information; business intelligence and data exploration; data processing analysis

Special Issue Information

Dear Colleagues,

Machine learning, artificial intelligence and a wide field of related technologies (e.g., in data science and intelligent systems) have significantly contributed to research into sustainability. They have provided breakthrough concepts, state-of-the-art technology and a wide range of innovations to tackle the problems we face.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Machine learning and AI for environment and health;
  • Machine learning and AI for agriculture and industry 4.0;
  • Machine learning and AI for air, water and climate sustainability;
  • Machine learning and AI for smart energy, renewable energy and green fuel;
  • Machine learning and AI for smart cities;
  • Machine learning and AI for sustainable policy making;
  • Machine learning and AI for traffic management and transportation;
  • Machine learning benchmark datasets, platforms and tools for sustainability research.

We look forward to receiving your contributions.

Dr. Wei-Chen Wu
Dr. Jason C. Hung
Dr. Yuchih Wei
Dr. Jui-hung Kao
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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
  • artificial intelligence
  • sustainability
  • deep learning
  • intelligent systems
  • industry 4.0
  • robotics
  • smart city
  • edge computing
  • data science
  • cognitive computing
  • big data

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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14 pages, 2858 KiB  
Article
An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors
by Damaris Mulwa, Benedicto Kazuzuru, Gerald Misinzo and Benard Bett
Big Data Cogn. Comput. 2024, 8(11), 148; https://doi.org/10.3390/bdcc8110148 - 30 Oct 2024
Viewed by 942
Abstract
Reports of Rift Valley fever (RVF), a highly climate-sensitive zoonotic disease, have been rather frequent in Kenya. Although multiple empirical analyses have shown that machine learning methods outperform time series models in forecasting time series data, there is limited evidence of their application [...] Read more.
Reports of Rift Valley fever (RVF), a highly climate-sensitive zoonotic disease, have been rather frequent in Kenya. Although multiple empirical analyses have shown that machine learning methods outperform time series models in forecasting time series data, there is limited evidence of their application in predicting disease outbreaks in Africa. In recent times, the literature has reported several applications of machine learning in facilitating intelligent decision-making within the healthcare sector and public health. However, there is a scarcity of information regarding the utilization of the XGBoost model for predicting disease outbreaks. Within the provinces of Kenya, the incidence of Rift Valley fever was more prominent in the Rift Valley (26.80%) and Eastern (20.60%) regions. This study investigated the correlation between the occurrence of RVF (rapid vegetation failure) and several climatic variables, including humidity, clay content, elevation, slope, and rainfall. The correlation matrix revealed a modest linear dependence between different climatic variables and RVF cases, with the highest correlation, a mere 0.02903, observed for rainfall. The XGBoost model was trained using these climate variables and achieved outstanding performance measures including an AUC of 0.8908, accuracy of 99.74%, precision of 99.75%, and recall of 99.99%. The analysis of feature importance revealed that rainfall was the most significant predictor. These findings align with previous studies demonstrating the significance of weather conditions in RVF outbreaks. The study’s results indicate that incorporating advanced machine learning models that consider several climatic variables can significantly enhance the prediction and management of RVF incidence. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)
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21 pages, 2212 KiB  
Article
Factors Affecting Single and Multivehicle Motorcycle Crashes: Insights from Day and Night Analysis Using XGBoost-SHAP Algorithm
by Panuwat Wisutwattanasak, Chamroeun Se, Thanapong Champahom, Rattanaporn Kasemsri, Sajjakaj Jomnonkwao and Vatanavongs Ratanavaraha
Big Data Cogn. Comput. 2024, 8(10), 128; https://doi.org/10.3390/bdcc8100128 - 3 Oct 2024
Viewed by 1042
Abstract
This study aimed to identify and compare the risk factors associated with motorcycle crash severity during both daytime and nighttime, for single and multivehicle incidents in Thailand using 2021–2024 data. The research employed the XGBoost (Extreme Gradient Boosting) method for statistical analysis and [...] Read more.
This study aimed to identify and compare the risk factors associated with motorcycle crash severity during both daytime and nighttime, for single and multivehicle incidents in Thailand using 2021–2024 data. The research employed the XGBoost (Extreme Gradient Boosting) method for statistical analysis and extensively examined the temporal instability of risk factors. The results highlight the importance of features impacting the injury severity of roadway collisions across various conditions. For single motorcycle crashes, the key risk factors included speeding, early morning incidents, off-road events, and long holidays. In multivehicle crashes, rear-end collisions, interactions with large vehicles, and collisions involving other motorcycles or passenger cars were linked to increased injury severity. The findings indicate that the important factors associated with motorcyclist injury severity in roadway crashes vary depending on the type of crash and time of day. These insights are valuable for policymakers and relevant authorities in developing targeted interventions to enhance road safety and mitigate the incidence of severe and fatal motorcycle crashes. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)
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12 pages, 33498 KiB  
Article
Deep-Learning-Driven Turbidity Level Classification
by Iván Trejo-Zúñiga, Martin Moreno, Rene Francisco Santana-Cruz and Fidel Meléndez-Vázquez
Big Data Cogn. Comput. 2024, 8(8), 89; https://doi.org/10.3390/bdcc8080089 - 7 Aug 2024
Viewed by 1488
Abstract
Accurate turbidity classification is essential for maintaining water quality in various contexts, from drinking water to industrial processes. Traditional turbidimeters face challenges, including interference from colored substances, particle shape and size variations, and the need for regular calibration and maintenance. This paper implements [...] Read more.
Accurate turbidity classification is essential for maintaining water quality in various contexts, from drinking water to industrial processes. Traditional turbidimeters face challenges, including interference from colored substances, particle shape and size variations, and the need for regular calibration and maintenance. This paper implements a convolutional neural network (CNN) to classify water samples based on their turbidity levels. The dataset consisted of images captured under controlled laboratory conditions, with turbidity levels measured using a 2100P Portable Turbidimeter. The CNN achieved a classification accuracy of 97.00% in laboratory settings. When tested on real-world water body samples, the model maintained an accuracy of 85.00%. The results demonstrate that deep learning can effectively classify turbidity levels, offering a promising solution to overcome the limitations of traditional methods. The study highlights the potential of CNNs for accurate and efficient turbidity measurement, balancing accuracy with practical applicability in field conditions. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)
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21 pages, 2914 KiB  
Article
Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification
by Yuri Gordienko, Yevhenii Trochun and Sergii Stirenko
Big Data Cogn. Comput. 2024, 8(7), 75; https://doi.org/10.3390/bdcc8070075 - 8 Jul 2024
Viewed by 1414
Abstract
By utilizing hybrid quantum–classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical [...] Read more.
By utilizing hybrid quantum–classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs’ training process. Understanding the role of quanvolutional operations and how they interact with classical neural networks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite Images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neural networks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, this study provides evidence of the potential advantages of HNNs in certain scenarios. Overall, the findings of this research contribute to advancing sustainable applications of machine learning by proposing novel techniques, optimizing model architectures, and demonstrating the feasibility of adopting HNNs for real-world image classification problems. These insights can inform the development of more efficient and environmentally friendly machine learning solutions. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)
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Review

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23 pages, 377 KiB  
Review
Application of Task Allocation Algorithms in Multi-UAV Intelligent Transportation Systems: A Critical Review
by Marco Rinaldi, Sheng Wang, Renan Sanches Geronel and Stefano Primatesta
Big Data Cogn. Comput. 2024, 8(12), 177; https://doi.org/10.3390/bdcc8120177 - 2 Dec 2024
Viewed by 885
Abstract
Unmanned aerial vehicles (UAVs), commonly known as drones, are being seen as the most promising type of autonomous vehicles in the context of intelligent transportation system (ITS) technology. A key enabling factor for the current development of ITS technology based on autonomous vehicles [...] Read more.
Unmanned aerial vehicles (UAVs), commonly known as drones, are being seen as the most promising type of autonomous vehicles in the context of intelligent transportation system (ITS) technology. A key enabling factor for the current development of ITS technology based on autonomous vehicles is the task allocation architecture. This approach allows tasks to be efficiently assigned to robots of a multi-agent system, taking into account both the robots’ capabilities and service requirements. Consequently, this study provides an overview of the application of drones in ITSs, focusing on the applications of task allocation algorithms for UAV networks. Currently, there are different types of algorithms that are employed for task allocation in drone-based intelligent transportation systems, including market-based approaches, game-theory-based algorithms, optimization-based algorithms, machine learning techniques, and other hybrid methodologies. This paper offers a comprehensive literature review of how such approaches are being utilized to optimize the allocation of tasks in UAV-based ITSs. The main characteristics, constraints, and limitations are detailed to highlight their advantages, current achievements, and applicability to different types of UAV-based ITSs. Current research trends in this field as well as gaps in the literature are also thoughtfully discussed. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)
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