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Research on Sustainability and Artificial Intelligence

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 43026

Special Issue Editor


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Guest Editor
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
Interests: artificial intelligence; deep learning; medical image processing; pattern recognition; transfer learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

Artificial intelligence (AI) has already shown its capacity to resolve realistic sustainability-related problems, and it has successfully contributed to many fields in terms of economy, business, management, environment, resources, ecology, society and individuals, energy, etc.

Recent advances in AI features include the invention of deep neural networks, which are a subset of machine learning, using a model inspired by the structure of the brain. It has been rapidly developed in recent years, in terms of both methodological development and practical applications. It provides computational models of multiple processing neural-network layers to learn and represent data with multiple levels of abstraction. It can implicitly capture intricate structures of large-scale data and is ideally suited to some of the hardware architectures that are currently available.

This problem-solving oriented Special Issue will cover a broad range of topics, applying the latest and emerging theoretical and technical advancements of artificial intelligence, particularly deep learning methods, to various realistic sustainability problems.

Topics may include, but are not limited to applying the following AI methods:

  • Traditional AI and big data analytic methods
  • Theoretical understanding of deep learning
  • Transfer learning, disentangling task transfer learning, and multi-task learning
  • Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming
  • Optimization by deep neural networks
  • Design of new loss functions, design of new activation functions, design of new layers
  • Explainable AI (XAI) and visualization methods
  • New model or improved model of convolutional neural network

in the following areas:

  • Sustainable construction, sustainable manufacturing
  • Sustainable healthcare, sustainable education
  • Sustainable urban and rural development
  • Blockchain
  • Human geography and social sustainability
  • Clean energy
  • Sustainable transportation
  • Supply chain management
  • Water and air pollution
  • Sustainable chemistry
  • Smart cities, smart manufacturing
  • Sustainable culture and heritage
  • Climate predictions and control
  • Sustainable agriculture, food, wildlife, agroengineering
  • Sustainable science and engineering

Prof. Yu-Dong Zhang
Guest Editor

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

  • Artificial intelligence
  • Deep learning
  • Transfer learning
  • Sustainable healthcare
  • Sustainable transportation
  • Sustainable agriculture

Published Papers (2 papers)

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29 pages, 3412 KiB  
Review
A Review of Deep-Learning-Based Medical Image Segmentation Methods
by Xiangbin Liu, Liping Song, Shuai Liu and Yudong Zhang
Sustainability 2021, 13(3), 1224; https://doi.org/10.3390/su13031224 - 25 Jan 2021
Cited by 363 | Viewed by 35670
Abstract
As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based [...] Read more.
As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems. Full article
(This article belongs to the Special Issue Research on Sustainability and Artificial Intelligence)
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21 pages, 16307 KiB  
Article
A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection
by Muhammad Rashid, Muhammad Attique Khan, Majed Alhaisoni, Shui-Hua Wang, Syed Rameez Naqvi, Amjad Rehman and Tanzila Saba
Sustainability 2020, 12(12), 5037; https://doi.org/10.3390/su12125037 - 19 Jun 2020
Cited by 124 | Viewed by 6361
Abstract
With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is [...] Read more.
With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is said to be sustainable and it has become a major area of research for the computer vision research community in the past few years. In this work, we present a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification. The proposed approach comprises three steps: (1) By utilizing two deep learning architectures, Very Deep Convolutional Networks for Large-Scale Image Recognition and Inception V3, it extracts features based on transfer learning, (2) Fusion of all the extracted feature vectors is performed by means of a parallel maximum covariance approach, and (3) The best features are selected using Multi Logistic Regression controlled Entropy-Variances method. For verification of the robust selected features, the Ensemble Learning method named Subspace Discriminant Analysis is utilized as a fitness function. The experimental process is conducted using four publicly available datasets, including Caltech-101, Birds database, Butterflies database and CIFAR-100, and a ten-fold validation process which yields the best accuracies of 95.5%, 100%, 98%, and 68.80% for the datasets respectively. Based on the detailed statistical analysis and comparison with the existing methods, the proposed selection method gives significantly more accuracy. Moreover, the computational time of the proposed selection method is better for real-time implementation. Full article
(This article belongs to the Special Issue Research on Sustainability and Artificial Intelligence)
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