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Special Issues
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: 13 August 2024 | Viewed by 1440

Special Issue Editors

Dr. Wei-Chen Wu

E-Mail Website
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

Published Papers (1 paper)

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Research

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 648
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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